Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 Evaluation of the Effect of Moringa Oleifera L. Medicinal Plant Extract and Zeatin on In vitro Culture of Phalaenopsis Orchid Plant Evaluation of the Effect of Moringa Oleifera L. Medicinal Plant Extract and Zeatin on In vitro Culture of Phalaenopsis Orchid Plant 1 32 4661 10.22103/jab.2024.23849.1585 FA Zahra Mirsaberi M.S. Student, Department of Horticultural Science and Engineering, Faculty of Agriculture and Natural Resources, University of Hormozgan, Bandar Abbas, Iran. Mojgan Soleimanizadeh Assistant Professor, Department of Horticultural Science and Engineering, Faculty of Agriculture and Natural Resources, University of Hormozgan, Bandar Abbas, Iran. 0000-0002-1327-2481 Abbas Yadollahi Department of Horticultural Sciences, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran Journal Article 2024 08 06 Introduction: Phalaenopsis orchid is known as the most popular orchid species in the horticulture industry due to its large, colorful and durable flowers, as well as its high adaptability, and it has a high economic value in international flower markets. Plant tissue culture technology has been widely used for rapid and high-scale propagation of this species. Moringa leaf is rich in natural cytokine hormones (zeatin), ascorbic acid and minerals such as calcium, potassium and iron, which can support Phalaenopsis orchid in vitro culture. Therefore, the purpose of this research is to evaluate the effect of Moringa oleifera and zeatin extract on the in vitro growth of Phalaenopsis orchid.<br />Material and methods: In order to carry out this research, after preparing Moringa medicinal plant extracts, different concentrations of plant extract and zeatin hormone were prepared and will be applied on Phalaenopsis orchid cultivation medium. After measuring the measured traits (including number of leaves, number of roots, leaf length, root length, seedling height, percentage of live, infected and browned seedlings), data analysis was done. <br />Results: The results showed that leaves were formed on some flowering stems after about two months. The contamination percentage of explants reached zero by the fourth disinfection treatment (S4). In this sterilization method, the combination of benomyl fungicide and mercury chloride was used, and contamination was removed by using these substances. Also, the highest rate of regeneration was observed in the fourth disinfection method and the vertical orientation of the explant (S4D2). The addition of 150 mg/liter of moringa leaf extract and 1.5 mg/liter of zeatin increased the number of leaves. Moringa leaf extract significantly increased root number, leaf length, root length and fresh and dry weight of seedlings and antioxidant activity. Moringa treatment with different concentrations reduced the percentage of orchid seedlings contamination to zero. Also, the application of moringa prevented the browning of the seedling. <br />Conclusion: In general, the treatment of moringa leaf extract with a concentration of 150 mg/L is suggested as an alternative to zeatin hormone in tissue culture of orchids and other ornamental plants. <br />Keywords: Tissue culture technology, Moringa Oleifera, Phalaenopsis Orchid, Plant Extract, Zeatin Introduction: Phalaenopsis orchid is known as the most popular orchid species in the horticulture industry due to its large, colorful and durable flowers, as well as its high adaptability, and it has a high economic value in international flower markets. Plant tissue culture technology has been widely used for rapid and high-scale propagation of this species. Moringa leaf is rich in natural cytokine hormones (zeatin), ascorbic acid and minerals such as calcium, potassium and iron, which can support Phalaenopsis orchid in vitro culture. Therefore, the purpose of this research is to evaluate the effect of Moringa oleifera and zeatin extract on the in vitro growth of Phalaenopsis orchid.<br />Material and methods: In order to carry out this research, after preparing Moringa medicinal plant extracts, different concentrations of plant extract and zeatin hormone were prepared and will be applied on Phalaenopsis orchid cultivation medium. After measuring the measured traits (including number of leaves, number of roots, leaf length, root length, seedling height, percentage of live, infected and browned seedlings), data analysis was done. <br />Results: The results showed that leaves were formed on some flowering stems after about two months. The contamination percentage of explants reached zero by the fourth disinfection treatment (S4). In this sterilization method, the combination of benomyl fungicide and mercury chloride was used, and contamination was removed by using these substances. Also, the highest rate of regeneration was observed in the fourth disinfection method and the vertical orientation of the explant (S4D2). The addition of 150 mg/liter of moringa leaf extract and 1.5 mg/liter of zeatin increased the number of leaves. Moringa leaf extract significantly increased root number, leaf length, root length and fresh and dry weight of seedlings and antioxidant activity. Moringa treatment with different concentrations reduced the percentage of orchid seedlings contamination to zero. Also, the application of moringa prevented the browning of the seedling. <br />Conclusion: In general, the treatment of moringa leaf extract with a concentration of 150 mg/L is suggested as an alternative to zeatin hormone in tissue culture of orchids and other ornamental plants. <br />Keywords: Tissue culture technology, Moringa Oleifera, Phalaenopsis Orchid, Plant Extract, Zeatin https://jab.uk.ac.ir/article_4661_f621585df244e9596dc70a39b579efb1.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 Effect of drought stress on different classes of chitinase genes expression in potato (Solanum tuberosum. L.) leaves Effect of drought stress on different classes of chitinase genes expression in potato (Solanum tuberosum. L.) leaves 33 48 4662 10.22103/jab.2024.22516.1525 FA Maryam Faramarzi Jafar Beiglou Ph.D. Student, Department of Plant Production and Genetic Engineering, Faculty of Agriculture, Lorestan University, Khorramabad, Iran. 0000-0001-5385-5131 Farhad Nazarian-Firouzabadi Professor, Department of Plant Production and Genetic Engineering, Faculty of Agriculture, Lorestan University, Khorramabad, Iran. 0000-0002-8291-3887 Seyed Sajad Sohrabi Assistant Professor, Department of Plant Production and Genetic Engineering, Faculty of Agriculture, Lorestan University, Khorramabad, Iran. Ali Moghadam Assistant Professor, Institute of biotechnology, Shiraz University, Iran. Journal Article 2023 11 15 Objective<br />Potato (Solanum tuberosum. L.) belongs to the Solanaceae family. It is the third important crop plant as human food source after wheat and rice. Several biotic and abiotic stresses affect potato production and reduce its potential yield. In the drought stress and dehydration, the expression level of many genes changes and the accumulation of stress-related proteins is affected. Chitinases are proteins that show a basic level of expression in normal conditions, but their expression increases dramatically in disease conditions and some abiotic stresses. The existence of great diversity in plant chitinases and its induction by a wide range of biotic and abiotic factors indicates their important role in the functions related to defense and stress. According to the Economic importance and Limiting effects of drought stress for plant growght in this research, the effect of drought stress on the expression level of different classes of chitinase gene was investigated. <br />Material and methods<br />In this research, the members of chitinase gene family was identified in potato genome by bioinformatics and computational methods. Then, one gene was selected from each class of chitinase gene based on RNA-Seq data analysis in drought stress, and their expression level was evaluated following a water deficit treatment (50% field capacity) by Real-time PCR analysis. The expression level of genes was measured using the Livak and Schmittgen method using the 2-ΔΔct formula.<br />Results<br />Under drought stress conditions, the majority of chitinase gene classes exhibited distinct expression patterns. Notably, among the four classes of identified chitinase genes in potato, class I exhibited up-regulation, whereas class V displayed a down-regulated trend.<br />Conclusion<br />In conclusion, our findings suggest a significant role for chitinases in potato's response to drought stress. The outcomes of this study offer valuable insights for screening potato cultivars/genotypes for drought tolerance and provide a foundation for molecular genetic strategies aimed at engineering drought-resistant potatoes. Objective<br />Potato (Solanum tuberosum. L.) belongs to the Solanaceae family. It is the third important crop plant as human food source after wheat and rice. Several biotic and abiotic stresses affect potato production and reduce its potential yield. In the drought stress and dehydration, the expression level of many genes changes and the accumulation of stress-related proteins is affected. Chitinases are proteins that show a basic level of expression in normal conditions, but their expression increases dramatically in disease conditions and some abiotic stresses. The existence of great diversity in plant chitinases and its induction by a wide range of biotic and abiotic factors indicates their important role in the functions related to defense and stress. According to the Economic importance and Limiting effects of drought stress for plant growght in this research, the effect of drought stress on the expression level of different classes of chitinase gene was investigated. <br />Material and methods<br />In this research, the members of chitinase gene family was identified in potato genome by bioinformatics and computational methods. Then, one gene was selected from each class of chitinase gene based on RNA-Seq data analysis in drought stress, and their expression level was evaluated following a water deficit treatment (50% field capacity) by Real-time PCR analysis. The expression level of genes was measured using the Livak and Schmittgen method using the 2-ΔΔct formula.<br />Results<br />Under drought stress conditions, the majority of chitinase gene classes exhibited distinct expression patterns. Notably, among the four classes of identified chitinase genes in potato, class I exhibited up-regulation, whereas class V displayed a down-regulated trend.<br />Conclusion<br />In conclusion, our findings suggest a significant role for chitinases in potato's response to drought stress. The outcomes of this study offer valuable insights for screening potato cultivars/genotypes for drought tolerance and provide a foundation for molecular genetic strategies aimed at engineering drought-resistant potatoes. https://jab.uk.ac.ir/article_4662_f64b2463cf1dba199491c885dff932f3.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 SCMV-based Overexpression of Insecticidal Proteins in Maize and Evaluation of their Effects against Fall Armyworm SCMV-based Overexpression of Insecticidal Proteins in Maize and Evaluation of their Effects against Fall Armyworm 49 76 4663 10.22103/jab.2024.22853.1547 FA Mahdieh Sadeghian Department of Plant Breeding and Biotechnology, Faculty of Agriculture, University of Zabol, Zabol, Iran Mahmood Solouki Department of Plant Breeding and Biotechnology, Faculty of Agriculture, University of Zabol Jafar Zolala Department of Agricultural Biotechnology, Faculty of Agriculture, Shahid Bahonar University of Kerman 0000-0002-5663-1448 Abbasali Emamjomeh Biotechnology and plant breeding, Faculty of Agriculture, University of Zabol, Zabol, Iran Georg Jander Boyce Thompson Institute for Plant Research, Ithaca, NY, USA Journal Article 2024 01 23 Objectives: Zea maiys L. is a key cereal worldwide which its yield is threatened severely by numerous herbivorous insects such as notorious fall armyworm (Spodoptera frugiperda Smith.). Current management strategies are basically depended upon costly and time-consuming methods such as chemicals and transgenic maize plants resulted in resistant pest populations in addition to health and environmental concerns. Transient In-planta expression of novel insecticidal proteins using viral vectors can explore the most efficient candidates against fall armyworm under time-saving, cost-effective and more real conditions. Present study aimed to investigate the effects of three insecticidal proteins including PTA (Pinellia ternata agglutinin), OAIP-1 and NcIa (toxins derived from two spiders’ venom) against fall armyworm using Sugarcane Mosaic Virus vector. <br />Materials and Methods: Coding sequences of considered insecticidal proteins were inserted into pSCMV vector between P1 and HC-Pro and recombinant constructs agroinjeced into maize seedlings. Successful overexpression of the proteins was confirmed by RT-PCR and qRT-PCR. In-planta insect bioassays were performed and weight gain of fall armyworm larvae was compared between infected and control plants after seven and 14 days of feeding. <br />Results: Overexpression of foreign genes was confirmed by RT-PCR and qRT-PCR results. Results of in planta insect bioassays showed that SCMV-based overexpression of insecticidal proteins in maize plants caused a statically significant reduction in weight gain of fall armyworm larvae in comparison with controls. After seven of feeding on plants expressing NcIa and PTA and OAIP-1 larval weight showed a reduction by 52, 54 and 31%, respectively, in comparison with GFP expressing plants. These results were stable after two weeks of larval feeding for two spider toxins but declined to 31% for PTA.<br />Conclusion: In conclusion, under in planta condition, PTA, NcIa and OAIP-1 were efficiently affected fall armyworm growth. They could be considered individually or in fusions (lectin- spider toxin) for further investigations to engineer maize resistance to fall armyworm. Objectives: Zea maiys L. is a key cereal worldwide which its yield is threatened severely by numerous herbivorous insects such as notorious fall armyworm (Spodoptera frugiperda Smith.). Current management strategies are basically depended upon costly and time-consuming methods such as chemicals and transgenic maize plants resulted in resistant pest populations in addition to health and environmental concerns. Transient In-planta expression of novel insecticidal proteins using viral vectors can explore the most efficient candidates against fall armyworm under time-saving, cost-effective and more real conditions. Present study aimed to investigate the effects of three insecticidal proteins including PTA (Pinellia ternata agglutinin), OAIP-1 and NcIa (toxins derived from two spiders’ venom) against fall armyworm using Sugarcane Mosaic Virus vector. <br />Materials and Methods: Coding sequences of considered insecticidal proteins were inserted into pSCMV vector between P1 and HC-Pro and recombinant constructs agroinjeced into maize seedlings. Successful overexpression of the proteins was confirmed by RT-PCR and qRT-PCR. In-planta insect bioassays were performed and weight gain of fall armyworm larvae was compared between infected and control plants after seven and 14 days of feeding. <br />Results: Overexpression of foreign genes was confirmed by RT-PCR and qRT-PCR results. Results of in planta insect bioassays showed that SCMV-based overexpression of insecticidal proteins in maize plants caused a statically significant reduction in weight gain of fall armyworm larvae in comparison with controls. After seven of feeding on plants expressing NcIa and PTA and OAIP-1 larval weight showed a reduction by 52, 54 and 31%, respectively, in comparison with GFP expressing plants. These results were stable after two weeks of larval feeding for two spider toxins but declined to 31% for PTA.<br />Conclusion: In conclusion, under in planta condition, PTA, NcIa and OAIP-1 were efficiently affected fall armyworm growth. They could be considered individually or in fusions (lectin- spider toxin) for further investigations to engineer maize resistance to fall armyworm. https://jab.uk.ac.ir/article_4663_c82a7178ece03ba6ee8051cc36691bdc.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 The impact of chitosan on the activation of genes related to resistance against rice sheath blight caused by Rhizoctonia solani The impact of chitosan on the activation of genes related to resistance against rice sheath blight caused by Rhizoctonia solani 77 98 4664 10.22103/jab.2024.23050.1555 FA Milad Habibi Daroonkolaee Department of Plant Protection. Sari Agricultural Sciences and Natural Resources University. Sari. Iran Valiollah Babaeizad Department of Plant Protection, Sari Agricultural Sciences and Natural Resources University 0000-0002-0434-2847 Mohammad Ali Tajik Department of Plant Protection. Sari Agricultural Sciences and Natural Resources University. Sari. Iran Heshmatolah Rahimian Department of Plant Protection, Sari University of Agricultural Sciences and Natural Resources, Sari, Iran Ali Dehestani Genetic and Agricultural Biotechnologhy Institute of Tabarestan, Sari. Iran 0000-0002-8845-7800 Journal Article 2024 03 06 Objective<br />Rice is a staple food for half the world's population. The sheath blight disease caused by Rhizoctonia solani poses a significant threat to rice production. While fungicides are commonly used to control plant diseases, they pose significant risks to human health and the environment. Therefore, finding biocompatible compounds that are effective in disease control is essential. Chitosan, a biocompatible compound, has been demonstrated in various studies to reduce damage. The objective of this research is to explore the induction of resistance in two domestic varieties of rice using chitosan against the fungus responsible for pod burn.<br />Materials and Methods<br />In the research, rice seedlings (cultivar Tarom and Khazar) were cultured and treated with chitosan. After 48 hours, the seedlings were infected with R. solani. Samples were collected at 0, 24, 48, 72, and 96 hours after the pathogen treatment. Total RNA was extracted from the samples, and cDNA was synthesized. Gene transcript analysis was conducted using the qPCR technique with specific primers PAL, LOX, PR1, PR3, and PR5 genes.<br />Results<br />The variance analysis results indicated that changes in the expression levels of PAL, LOX, PR1, PR3, and PR5 genes were significant across all sources. Significant differences were observed in the expression levels of all genes. This suggests that genes in the resistant cultivar have a higher expression potential compared to the sensitive cultivar, leading to faster and more extensive-expression during contamination.<br />Conclusion<br />In this study, chitosan increased the expression of PAL, LOX, PR1, PR3 and PR5 genes in treated plants compared to control plants. The results of this study demonstrate that applying one gram per liter of chitosan to the aerial parts of plants induces proteins related to pathogenicity and creates physical and chemical barriers against pathogens. Consequently, it can be utilized in agricultural management to decrease R. solani contamination in rice and is a viable alternative to fungicides. Objective<br />Rice is a staple food for half the world's population. The sheath blight disease caused by Rhizoctonia solani poses a significant threat to rice production. While fungicides are commonly used to control plant diseases, they pose significant risks to human health and the environment. Therefore, finding biocompatible compounds that are effective in disease control is essential. Chitosan, a biocompatible compound, has been demonstrated in various studies to reduce damage. The objective of this research is to explore the induction of resistance in two domestic varieties of rice using chitosan against the fungus responsible for pod burn.<br />Materials and Methods<br />In the research, rice seedlings (cultivar Tarom and Khazar) were cultured and treated with chitosan. After 48 hours, the seedlings were infected with R. solani. Samples were collected at 0, 24, 48, 72, and 96 hours after the pathogen treatment. Total RNA was extracted from the samples, and cDNA was synthesized. Gene transcript analysis was conducted using the qPCR technique with specific primers PAL, LOX, PR1, PR3, and PR5 genes.<br />Results<br />The variance analysis results indicated that changes in the expression levels of PAL, LOX, PR1, PR3, and PR5 genes were significant across all sources. Significant differences were observed in the expression levels of all genes. This suggests that genes in the resistant cultivar have a higher expression potential compared to the sensitive cultivar, leading to faster and more extensive-expression during contamination.<br />Conclusion<br />In this study, chitosan increased the expression of PAL, LOX, PR1, PR3 and PR5 genes in treated plants compared to control plants. The results of this study demonstrate that applying one gram per liter of chitosan to the aerial parts of plants induces proteins related to pathogenicity and creates physical and chemical barriers against pathogens. Consequently, it can be utilized in agricultural management to decrease R. solani contamination in rice and is a viable alternative to fungicides. https://jab.uk.ac.ir/article_4664_fc452d063a72e0824cacf90a32c3e358.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 Evaluation of Genetic Diversity of Suaeda aegyptiaca Genotypes in Khuzestan Province Using SCoT Molecular Markers Evaluation of Genetic Diversity of Suaeda aegyptiaca Genotypes in Khuzestan Province Using SCoT Molecular Markers 99 120 4665 10.22103/jab.2024.22888.1549 FA Zahra Haghighipor Graduate Student, Department of Agronomy and Plant Breeding, Faculty of Agriculture, Yasouj University, Yasouj, Iran. Massoud Dehdari Associate Professor, Department of Agronomy and Plant Breeding, Faculty of Agriculture, Yasouj University, Yasouj, Iran. 0000-0002-2717-8576 Fatemeh Nasernakhaei Assistant Professor, Department of Genetic and Plant Production, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran. 0000-0002-7714-4331 Reza Amiri Fahliani Associate Professor, Department of Agronomy and Plant Breeding, Faculty of Agriculture, Yasouj University, Yasouj, Iran. 0000-0001-6137-897X Journal Article 2024 02 01 Objective<br />Suaeda aegyptiaca (Hasselq.) Zohary is a halophyte plant with many nutritional and medicinal uses. There is no information on study and estimation of the genetic diversity of S. aegyptiaca plants in Khuzestan province. So, this study was designed and implemented with the aim of investigating the genetic diversity and genetic relationships among S. aegyptiaca genotypes in Khuzestan province. <br />Materials and methods<br />In order to study of genetic diversity and genetic relationships among S. aegyptiaca genotypes, 26 genotypes from two regions of Khuzestan were selected as group one and two based on their DNA quantity and quality from the 71 collected samples. 12 SCoT primers were used to investigate the diversity and genetic relationships among and within populations. Genomic DNA was extracted from leaf tissue of genotypes and DNA amplification was done using 12 SCoT primers, the obtained bands were scored as zero (band absence) and 1 (band presence). Total number of bands, number of polymorphic bands, and percentage of polymorphic bands, band information index, resolution power, polymorphic information content, marker index and Shannon index were calculated.<br />Results<br />A total of 103 fragments were amplified, which showed a high percentage of polymorphism. SCoT14 had the highest polymorphic information (0.43) and marker index (4.36). The highest resolution power (Rp) belongs to SCoT13 (13.38). Cluster analysis using UPGMA method classified the genotypes into five groups. Karoun (KAR2) and Gheyzaniyeh (GH1) genotypes, with the highest genetic distance, were suitable for usage in breeding programs. Analysis of molecular variance (AMOVA) showed that genetic diversity within groups was higher than between groups. <br />Conclusions<br />Some of the studied markers showed a high ability to distinguish genotypes, also the results indicated a high genetic diversity among the genotypes in terms of the studied SCoT markers. These results can be used in the breeding programs of S. aegyptiaca and related species. Objective<br />Suaeda aegyptiaca (Hasselq.) Zohary is a halophyte plant with many nutritional and medicinal uses. There is no information on study and estimation of the genetic diversity of S. aegyptiaca plants in Khuzestan province. So, this study was designed and implemented with the aim of investigating the genetic diversity and genetic relationships among S. aegyptiaca genotypes in Khuzestan province. <br />Materials and methods<br />In order to study of genetic diversity and genetic relationships among S. aegyptiaca genotypes, 26 genotypes from two regions of Khuzestan were selected as group one and two based on their DNA quantity and quality from the 71 collected samples. 12 SCoT primers were used to investigate the diversity and genetic relationships among and within populations. Genomic DNA was extracted from leaf tissue of genotypes and DNA amplification was done using 12 SCoT primers, the obtained bands were scored as zero (band absence) and 1 (band presence). Total number of bands, number of polymorphic bands, and percentage of polymorphic bands, band information index, resolution power, polymorphic information content, marker index and Shannon index were calculated.<br />Results<br />A total of 103 fragments were amplified, which showed a high percentage of polymorphism. SCoT14 had the highest polymorphic information (0.43) and marker index (4.36). The highest resolution power (Rp) belongs to SCoT13 (13.38). Cluster analysis using UPGMA method classified the genotypes into five groups. Karoun (KAR2) and Gheyzaniyeh (GH1) genotypes, with the highest genetic distance, were suitable for usage in breeding programs. Analysis of molecular variance (AMOVA) showed that genetic diversity within groups was higher than between groups. <br />Conclusions<br />Some of the studied markers showed a high ability to distinguish genotypes, also the results indicated a high genetic diversity among the genotypes in terms of the studied SCoT markers. These results can be used in the breeding programs of S. aegyptiaca and related species. https://jab.uk.ac.ir/article_4665_7c0f63c15f8749d716ba1ac9121cc1a8.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 Engineering and production of recombinant ricin protein with the aim of producing effective immunotoxin Engineering and production of recombinant ricin protein with the aim of producing effective immunotoxin 121 138 4666 10.22103/jab.2024.23650.1578 FA Abdolla Rahmani Department of Biotechnology and Plant Breeding, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad Seyed Hasan Marashi Department of Biotechnology and Plant Breeding, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran 0000-0002-2499-6725 Nasrin Moshtaghi Associate Professor, Department of Biotechnology and Plant Breeding, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran. 0000-0003-1095-8450 Mohammad Reza Nassiry Professor, Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran. 0000-0001-7119-8155 Amin Mirshamsi Kakhki Department of Biotechnology and Plant Breeding, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Hamid Ariannezhad Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, P. O. Box: 91775-1163, Iran Journal Article 2024 06 29 Aim<br />Ricin is a globular glycoprotein poison found in the endosperm of the castor plant. This protein consists of two A chains with 267 amino acids (32 kilo Daltons) and a B chain with 262 amino acids (34 kilodaltons), connected by a disulfide bond. The sensitivity of cancer cells to ricin is due to the higher expression of glycoproteins on the surface of cancer cells. The toxicity of the ricin enzyme in destroying cells caused this enzyme to be used as the most important candidate in the production of immunotoxins. However, the low ability of ricin to enter the target cell using the Golgi apparatus has caused increased cytotoxicity, which affected its use as an immunotoxin. This research was carried out with the aim of engineering ricin protein to increase the surface charge of the protein and facilitate the entry of ricin into the cell through the endosomal route and reduce the effect of cytotoxicity.<br />Methods <br />Therefore, the structural status of ricin protein with R134A, L214A, R31A, and P250A mutations was investigated using thermodynamic studies. Then, the gene sequence related to the mutant version was synthesized and expressed using the expression vector pET22b(+) in the cell wall of Escherichia coli BL21(DE3). Purification was done using a Ni-NTA column, and then the function of the protein on class A431 cancer epithelial cells was evaluated using a cytotoxicity test. <br />Results <br />The obtained results showed that the considered mutations significantly increased the surface charge of the protein. Also, comparing the analysis of Root Mean Square Deviation (RMSD), root-mean-square fluctuations (RMSF) and Radius of gyration (Rg) graphs between the natural sample and the mutant showed that the mutations have no significant effect on the main structure of the protein. Confirmation of the production and purification of recombinant ricin protein in the cell wall with a molecular weight of about 33 kilodaltons was done using SDS-PAGE gel. Cytotoxicity results showed that mutant ricin protein can cause cell death of A431 cancer cells at a concentration of 300 ng/ml. <br />Conclusion <br />In silico and In vitro results on ricin protein showed that creating targeted mutations to increase protein surface charge can improve protein performance. Therefore, mutant ricin protein (R134A, L214A, P250A, and R31A) with greater cell penetration and less toxicity for target cells has a high ability to be used as a toxin in the production of immunotoxins. Aim<br />Ricin is a globular glycoprotein poison found in the endosperm of the castor plant. This protein consists of two A chains with 267 amino acids (32 kilo Daltons) and a B chain with 262 amino acids (34 kilodaltons), connected by a disulfide bond. The sensitivity of cancer cells to ricin is due to the higher expression of glycoproteins on the surface of cancer cells. The toxicity of the ricin enzyme in destroying cells caused this enzyme to be used as the most important candidate in the production of immunotoxins. However, the low ability of ricin to enter the target cell using the Golgi apparatus has caused increased cytotoxicity, which affected its use as an immunotoxin. This research was carried out with the aim of engineering ricin protein to increase the surface charge of the protein and facilitate the entry of ricin into the cell through the endosomal route and reduce the effect of cytotoxicity.<br />Methods <br />Therefore, the structural status of ricin protein with R134A, L214A, R31A, and P250A mutations was investigated using thermodynamic studies. Then, the gene sequence related to the mutant version was synthesized and expressed using the expression vector pET22b(+) in the cell wall of Escherichia coli BL21(DE3). Purification was done using a Ni-NTA column, and then the function of the protein on class A431 cancer epithelial cells was evaluated using a cytotoxicity test. <br />Results <br />The obtained results showed that the considered mutations significantly increased the surface charge of the protein. Also, comparing the analysis of Root Mean Square Deviation (RMSD), root-mean-square fluctuations (RMSF) and Radius of gyration (Rg) graphs between the natural sample and the mutant showed that the mutations have no significant effect on the main structure of the protein. Confirmation of the production and purification of recombinant ricin protein in the cell wall with a molecular weight of about 33 kilodaltons was done using SDS-PAGE gel. Cytotoxicity results showed that mutant ricin protein can cause cell death of A431 cancer cells at a concentration of 300 ng/ml. <br />Conclusion <br />In silico and In vitro results on ricin protein showed that creating targeted mutations to increase protein surface charge can improve protein performance. Therefore, mutant ricin protein (R134A, L214A, P250A, and R31A) with greater cell penetration and less toxicity for target cells has a high ability to be used as a toxin in the production of immunotoxins. https://jab.uk.ac.ir/article_4666_9f9e8cba3700df6a947a8cf91035ab84.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 Identification of pathways and lncRNAs involved in date palm flowering using RNA-Seq technique Identification of pathways and lncRNAs involved in date palm flowering using RNA-Seq technique 139 166 4667 10.22103/jab.2024.23423.1568 FA Shahin Madadi Ph. D. student of Biotechnology, Department of Plant Breeding and Biotechnology, University of Zabol, Zabol, Iran 0009-0005-3966-954X Salehe Ganjali Assistant Professor Department of Plant Breeding and Biotechnology, University of Zabol, Zabol, Iran 0000-0002-9824-9773 Leila Fahmideh Associate Professor of the Department of Plant Breeding and Biotechnology, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran 0000-0003-4140-4928 Hamed Hasanzadeh Khankahdani Horticultural Crops Research Department, Hormozgan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Bandar Abbas, Iran. 0000-0002-2679-4561 Hadis Kord Post-Doctoral Research, Department IPK Gatersleben Gemany 0009-0000-3996-7145 Reza Haghi Dareh Deh Post-Doctoral Research, Department IPK Gatersleben Gemany 0000-0002-9143-4319 Journal Article 2024 05 24 Objective<br />The date palm, known scientifically as Phoenix dactylifera L, grows well in tropical and subtropical regions. The process of flower development initiates sexual reproduction in plants. Specific genes regulate this process in the floral meristem, cycle, organ identity, and certain lncRNAs. Studies on animals have shown that lncRNAs are notably active in reproductive organs.<br />Materials and methods<br />We aimed to explore the pathways and lncRNAs involved in date palm flowering. Minab date palm flower bud samples were collected from the Tropical Fruit Research Station in Hormozgan Province, Iran. The samples were then transferred to the Tabaristan Agricultural Biotechnology Research Institute for analysis. RNA was extracted from various cultivars' male and female flower buds and combined equally. Two replicates were produced for each mixed sample. Subsequently, it was sent for sequencing.<br />Results<br />We scrutinized the sequencing outcomes to delve into the mechanisms of plant flowering which is dependent on seasonal cues and is influenced by environmental factors and specific genes. Our research focused on four key pathways: photoperiod, vernalization, self-stimulation, and gibberellin. Our investigation uncovered long non-coding RNAs (lncRNAs) associated with date palm flowering. We identified 13 lncRNAs in the photoperiod pathway, one in the self-stimulation, and four in the gibberellin pathway. Interestingly, no lncRNAs were detected in the vernalization pathway. We proceeded to compare these lncRNAs with those found in other plant species. The lncRNAs in the photoperiod pathway resembled those in oil palm, red clover, olive, and wheat. Their increased expression leads to accelerated flowering, irrespective of photoperiod. Moreover, we pinpointed a lncRNA in the gibberellin pathway that is similar to the one found in cotton which is linked to the LFY gene. Furthermore, our study revealed four lncRNAs in the self-stimulation pathway, resembling lncRNAs in apple, millet, and pea, all of which are associated with the FCA gene.<br />Conclusions<br />The flowering process in plants occurs only in certain seasons of the year, through regulatory networks resulting from environmental signals and involving the genes associated with the four regulatory pathways of flowering: photoperiod, vernalization, self-stimulation, and gibberellin. The identification and comparison of lncRNAs involved in different flowering pathways, along with the identification of related genes, provide the basis for future applied research in this valuable plant. Objective<br />The date palm, known scientifically as Phoenix dactylifera L, grows well in tropical and subtropical regions. The process of flower development initiates sexual reproduction in plants. Specific genes regulate this process in the floral meristem, cycle, organ identity, and certain lncRNAs. Studies on animals have shown that lncRNAs are notably active in reproductive organs.<br />Materials and methods<br />We aimed to explore the pathways and lncRNAs involved in date palm flowering. Minab date palm flower bud samples were collected from the Tropical Fruit Research Station in Hormozgan Province, Iran. The samples were then transferred to the Tabaristan Agricultural Biotechnology Research Institute for analysis. RNA was extracted from various cultivars' male and female flower buds and combined equally. Two replicates were produced for each mixed sample. Subsequently, it was sent for sequencing.<br />Results<br />We scrutinized the sequencing outcomes to delve into the mechanisms of plant flowering which is dependent on seasonal cues and is influenced by environmental factors and specific genes. Our research focused on four key pathways: photoperiod, vernalization, self-stimulation, and gibberellin. Our investigation uncovered long non-coding RNAs (lncRNAs) associated with date palm flowering. We identified 13 lncRNAs in the photoperiod pathway, one in the self-stimulation, and four in the gibberellin pathway. Interestingly, no lncRNAs were detected in the vernalization pathway. We proceeded to compare these lncRNAs with those found in other plant species. The lncRNAs in the photoperiod pathway resembled those in oil palm, red clover, olive, and wheat. Their increased expression leads to accelerated flowering, irrespective of photoperiod. Moreover, we pinpointed a lncRNA in the gibberellin pathway that is similar to the one found in cotton which is linked to the LFY gene. Furthermore, our study revealed four lncRNAs in the self-stimulation pathway, resembling lncRNAs in apple, millet, and pea, all of which are associated with the FCA gene.<br />Conclusions<br />The flowering process in plants occurs only in certain seasons of the year, through regulatory networks resulting from environmental signals and involving the genes associated with the four regulatory pathways of flowering: photoperiod, vernalization, self-stimulation, and gibberellin. The identification and comparison of lncRNAs involved in different flowering pathways, along with the identification of related genes, provide the basis for future applied research in this valuable plant. https://jab.uk.ac.ir/article_4667_fbad540b2f3b5638a9be9aa6a4d8e450.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 Effect of drought stress on morphological traits and proteome expression patterns in wheat leaf Effect of drought stress on morphological traits and proteome expression patterns in wheat leaf 167 188 4668 10.22103/jab.2024.22025.1504 FA Shaghayegh Aslzad MSc Student, Department of Plant Breeding and Biotechnology, Faculty of Agriculture, University of Tabriz, Tabriz. Iran. 0000-0001-6514-5321 Mahmoud Toorchi Professor of Plant Breeding and Biotechnology Department, Faculty of Agriculture, University of Tabriz, Tabriz. Iran. 0000-0001-7123-9914 Journal Article 2023 08 13 Recent advances in molecular biology methods have raised hopes that by identifying candidate proteins for drought tolerance, significant steps can be taken to increase wheat yield. The aim of this study is to investigate the effect of drought stress on morphological traits and leaf proteome pattern of the new wheat cultivar, Sadra.<br />Materials and methods<br />An experiment was conducted under greenhouse conditions and controlled conditions. Sadra cultivar was grown at two irrigation levels: normal irrigation and water deficit stress with 10 replications. Water deficit stress was applied to the stress treatment pots 20 days after planting. Analysis of wheat leaf tissue proteome was performed using two-dimensional electrophoresis and Coomassie blue staining. After spot detection, the t-test was performed for the volumetric percentage of protein spots as well as morphological data. Moreover, the upregulation and downregulation of protein spots due to water deficit stress were determined based on induction factor. Protein spots with expression changes were identified using mass spectrometry.<br />Results<br />Morphological data analysis showed statistically significant differences between irrigation levels for leaf area, plant height, fresh and dry weight of roots and aerial parts, and root volume. Two-dimensional electrophoresis analysis of leaf tissue proteome revealed that out of 136 discernible protein spots in Coomassie blue staining, 24 protein spots showed statistically significant expression changes under drought stress compared to normal conditions. Among these, 10 spots exhibited decreased expression, and 14 spots showed increased expression under water deficit conditions compared to the control. These protein spots were identified based on their isoelectric points and molecular weights and further confirmed using mass spectrometry. The identified proteins in this study were categorized into glycolysis, photosynthesis, electron transport chain, Calvin cycle, carbon metabolism, ROS scavenging and detoxification, cellular structure, and stress response pathways.<br />Conclusions<br />The findings suggest that drought stress significantly affects morphological traits and leaf proteome pattern in the Sadra wheat cultivar. Understanding the proteomic changes under drought stress can provide insights into the molecular mechanisms involved in wheat drought tolerance and contribute to the development of more resilient wheat cultivars. Recent advances in molecular biology methods have raised hopes that by identifying candidate proteins for drought tolerance, significant steps can be taken to increase wheat yield. The aim of this study is to investigate the effect of drought stress on morphological traits and leaf proteome pattern of the new wheat cultivar, Sadra.<br />Materials and methods<br />An experiment was conducted under greenhouse conditions and controlled conditions. Sadra cultivar was grown at two irrigation levels: normal irrigation and water deficit stress with 10 replications. Water deficit stress was applied to the stress treatment pots 20 days after planting. Analysis of wheat leaf tissue proteome was performed using two-dimensional electrophoresis and Coomassie blue staining. After spot detection, the t-test was performed for the volumetric percentage of protein spots as well as morphological data. Moreover, the upregulation and downregulation of protein spots due to water deficit stress were determined based on induction factor. Protein spots with expression changes were identified using mass spectrometry.<br />Results<br />Morphological data analysis showed statistically significant differences between irrigation levels for leaf area, plant height, fresh and dry weight of roots and aerial parts, and root volume. Two-dimensional electrophoresis analysis of leaf tissue proteome revealed that out of 136 discernible protein spots in Coomassie blue staining, 24 protein spots showed statistically significant expression changes under drought stress compared to normal conditions. Among these, 10 spots exhibited decreased expression, and 14 spots showed increased expression under water deficit conditions compared to the control. These protein spots were identified based on their isoelectric points and molecular weights and further confirmed using mass spectrometry. The identified proteins in this study were categorized into glycolysis, photosynthesis, electron transport chain, Calvin cycle, carbon metabolism, ROS scavenging and detoxification, cellular structure, and stress response pathways.<br />Conclusions<br />The findings suggest that drought stress significantly affects morphological traits and leaf proteome pattern in the Sadra wheat cultivar. Understanding the proteomic changes under drought stress can provide insights into the molecular mechanisms involved in wheat drought tolerance and contribute to the development of more resilient wheat cultivars. https://jab.uk.ac.ir/article_4668_6b9bb055c60428fa01686736b18f39fc.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 Assessment of expression pattern of some antioxidant and dehydrin genes in durum wheat genotypes under water deficit conditions Assessment of expression pattern of some antioxidant and dehydrin genes in durum wheat genotypes under water deficit conditions 189 208 4669 10.22103/jab.2024.24084.1619 FA Mandana Moradi Department of Plant Breeding and Biotechnology, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran Alireza Etminan Department of Plant breeding and Biotechnology, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran Reza Mohammadi Dryland Agricultural Research Institute, Sararood Branch, Agricultural Research, Education and Extension Organization (AREEO), Kermanshah, Iran. 0000-0001-7694-0849 Lia Shooshtari Department of Plant Breeding and Biotechnology, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran 0000-0003-0239-7040 Ali Mehras Mehrabi Department of Plant Breeding and Biotechnology, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran. Journal Article 2024 09 25 Abstract<br />Objective<br />Drought stress is one of the most important environmental stresses that has negative effects on plant growth and development, as well as finally yield performance. Durum wheat has a wide range of adaptability in the Mediterranean regions, where the water deficit is a main challenge to achieving high productivity. In the present study, to investigate the molecular response of some selected durum wheat genotypes from breeding programs, the expression patterns of some antioxidant genes such as catalase (CAT), ascorbate peroxidase (APX), guaiacol peroxidase (GPX), superoxide dismutase (SOD) along with two dehydrin genes including TdDHN16 and TdDHN15 were assessed under control, moderate, severe water deficit stress treatments.<br />Materials and methods<br />In the present study, the effects of different water deficit treatments on expression patterns of CAT, APX, GPX, SOD, TdDHN16, and TdDHN15 genes in six promising durum wheat genotypes along with a check cultivar (Zahab) were evaluated. The experiment was performed in an optimal growth conditions in an experimental glasshouse, and water deficit treatments were determined based on the filed capacity method. After seedling establishment and applying stress treatment (21-days), plants were subjected to sampling and the relative expression for targeted genes was estimated as proposed by Livak and Schmittgen (2001).<br />Results<br />According to results of combined analysis of variance, the significant differences were observed between water deficit stress treatments, genotypes, and their interaction in terms of relative expression of all studied genes. The highest increasing expression was observed in the moderate treatment for APX, GPX, and SOD genes and in the severe treatment for APX, TdDHN15, and GPX genes. A comparison of the expression patterns of studied genes revealed that tolerant genotypes (G2, G4, and G5) along with check cultivar (Zahab) showed highest relative expression than other genotypes. Hence, it seems that these genotypes have a high ability against oxidative stress.<br />Conclusion<br />Our results showed that the genotype G2 due to high ability in regulation of relative expression of antioxidant and dehydrin genes under water deficit stress treatments. Thus, complementary physiological and biochemical assays as well as evaluation of grain yield under field conditions of this genotype could provide useful information regarding to use of it as a drought-tolerant parent in breeding programs with emphasis on the transfer of desirable agronomic features. Abstract<br />Objective<br />Drought stress is one of the most important environmental stresses that has negative effects on plant growth and development, as well as finally yield performance. Durum wheat has a wide range of adaptability in the Mediterranean regions, where the water deficit is a main challenge to achieving high productivity. In the present study, to investigate the molecular response of some selected durum wheat genotypes from breeding programs, the expression patterns of some antioxidant genes such as catalase (CAT), ascorbate peroxidase (APX), guaiacol peroxidase (GPX), superoxide dismutase (SOD) along with two dehydrin genes including TdDHN16 and TdDHN15 were assessed under control, moderate, severe water deficit stress treatments.<br />Materials and methods<br />In the present study, the effects of different water deficit treatments on expression patterns of CAT, APX, GPX, SOD, TdDHN16, and TdDHN15 genes in six promising durum wheat genotypes along with a check cultivar (Zahab) were evaluated. The experiment was performed in an optimal growth conditions in an experimental glasshouse, and water deficit treatments were determined based on the filed capacity method. After seedling establishment and applying stress treatment (21-days), plants were subjected to sampling and the relative expression for targeted genes was estimated as proposed by Livak and Schmittgen (2001).<br />Results<br />According to results of combined analysis of variance, the significant differences were observed between water deficit stress treatments, genotypes, and their interaction in terms of relative expression of all studied genes. The highest increasing expression was observed in the moderate treatment for APX, GPX, and SOD genes and in the severe treatment for APX, TdDHN15, and GPX genes. A comparison of the expression patterns of studied genes revealed that tolerant genotypes (G2, G4, and G5) along with check cultivar (Zahab) showed highest relative expression than other genotypes. Hence, it seems that these genotypes have a high ability against oxidative stress.<br />Conclusion<br />Our results showed that the genotype G2 due to high ability in regulation of relative expression of antioxidant and dehydrin genes under water deficit stress treatments. Thus, complementary physiological and biochemical assays as well as evaluation of grain yield under field conditions of this genotype could provide useful information regarding to use of it as a drought-tolerant parent in breeding programs with emphasis on the transfer of desirable agronomic features. https://jab.uk.ac.ir/article_4669_ccc36675ce8d7286aff56ecccb53d0a5.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 A systematic review of internet of things-based smart farming applications with biotechnology A systematic review of internet of things-based smart farming applications with biotechnology 209 222 4670 10.22103/jab.2025.23992.1600 FA Kamlesh Kumar Yadav Department of CS & IT, Kalinga University, Raipur, India. 0009-0006-4665-5506 Dhablia Dharmesh Kirit Department of CS &amp; IT, Kalinga University, Raipur, India. 0009-0002-9200-211X Journal Article 2024 09 06 Objective<br />This study combines technology and software to let farmers track and change certain field parameters in real-time. Along with a quick examination of the interplay between weather station monitoring and mobile data logging, it offers a study on Smart Farming (SF) based on the Internet of Things (IoT). The Internet of Things (IoT) will play a pivotal role in the viability of the agriculture sector in the years to come. Highlighting technological, ICT, and robotics advancements, the research centers on multimedia devices, communication procedures, sensors, and systems frequently utilized in SF monitoring. In order support future researchers and provide the groundwork for the creation of automated IoT-based SF monitoring systems that incorporate biotechnology, this article describes the methodologies used in this area. This project aimed to enhance Smart Farming (SF) efficiency through IoT technologies, focusing on methods, processes, and tools for monitoring SF, integrating biotechnology for improved production and sustainability, and highlighting the role of automated processes and robots in IoT-based agricultural solutions. <br /><br />Results <br />This study proves that the Internet of Things (IoT) is becoming more significant in contemporary farming, outperforming conventional farming practices as a result of technological, information and communication technology (ICT), and robotics improvements. Internet of Things (IoT) integration into SF boosts productivity by allowing for condition monitoring and correction in real time. According to the results, sensors and communication systems, in conjunction with IoT technology, allow for the automated and exact administration of agricultural tasks. Data logging systems and multimedia devices work well together to gather and analyze agricultural data, which improves agricultural decision-making and yields better results.<br /><br />Conclusions<br />The Internet of Things (IoT) has the potential to change the nature of farming completely by facilitating the development of methods that are more productive, accurate, and environmentally friendly. It is believed that Smart Farming will continue to surpass conventional farming practices in terms of popularity as technology progresses. More efficient and automated farming systems will be the result of the effective integration of the Internet of Things (IoT), biotechnology, and robotics. This research provides important information for future studies on the improvement of SF surveillance through the Internet of Things (IoT). There is great promise for the future of agriculture and higher productivity in the agricultural industry as a whole with the introduction of robotics and automation. Objective<br />This study combines technology and software to let farmers track and change certain field parameters in real-time. Along with a quick examination of the interplay between weather station monitoring and mobile data logging, it offers a study on Smart Farming (SF) based on the Internet of Things (IoT). The Internet of Things (IoT) will play a pivotal role in the viability of the agriculture sector in the years to come. Highlighting technological, ICT, and robotics advancements, the research centers on multimedia devices, communication procedures, sensors, and systems frequently utilized in SF monitoring. In order support future researchers and provide the groundwork for the creation of automated IoT-based SF monitoring systems that incorporate biotechnology, this article describes the methodologies used in this area. This project aimed to enhance Smart Farming (SF) efficiency through IoT technologies, focusing on methods, processes, and tools for monitoring SF, integrating biotechnology for improved production and sustainability, and highlighting the role of automated processes and robots in IoT-based agricultural solutions. <br /><br />Results <br />This study proves that the Internet of Things (IoT) is becoming more significant in contemporary farming, outperforming conventional farming practices as a result of technological, information and communication technology (ICT), and robotics improvements. Internet of Things (IoT) integration into SF boosts productivity by allowing for condition monitoring and correction in real time. According to the results, sensors and communication systems, in conjunction with IoT technology, allow for the automated and exact administration of agricultural tasks. Data logging systems and multimedia devices work well together to gather and analyze agricultural data, which improves agricultural decision-making and yields better results.<br /><br />Conclusions<br />The Internet of Things (IoT) has the potential to change the nature of farming completely by facilitating the development of methods that are more productive, accurate, and environmentally friendly. It is believed that Smart Farming will continue to surpass conventional farming practices in terms of popularity as technology progresses. More efficient and automated farming systems will be the result of the effective integration of the Internet of Things (IoT), biotechnology, and robotics. This research provides important information for future studies on the improvement of SF surveillance through the Internet of Things (IoT). There is great promise for the future of agriculture and higher productivity in the agricultural industry as a whole with the introduction of robotics and automation. https://jab.uk.ac.ir/article_4670_d6a2be6d87d35c6d161fde16f21a5864.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 An implementation framework for food security using machine learning and biotechnology algorithms in precision agriculture and smart farming An implementation framework for food security using machine learning and biotechnology algorithms in precision agriculture and smart farming 223 236 4671 10.22103/jab.2025.23996.1604 FA Nidhi Mishra Department of CS & IT, Kalinga University, Raipur, India. 0009-0001-9755-7950 Priti Sharma Department of CS &amp; IT, Kalinga University, Raipur, India. 0009-0006-8132-8842 Journal Article 2024 09 06 Objective<br />Converting data into digital form has led to a massive influx of data in almost every industry that relies on data-driven operations. The digital data processing has significantly increased the volume of information being processed. The emergence of electronic agriculture management has profoundly impacted Information and Communication Technology (ICT), resulting in advantages for farmers and customers and driving the adoption of technological solutions in rural areas. This study emphasizes the promise of ICT technologies in conventional agriculture and the obstacles to their employment in farming operations.<br /><br />Results<br />This study emphasizes the promise of ICT technologies in conventional agriculture and the obstacles to their employment in farming operations. The research provides thorough information on automation, Internet of Things (IoT) gadgets, and challenges related to Machine Learning (ML). Drones are being contemplated for crop monitoring and production optimization in Precision Agriculture (PA) and Smart Farming (SF). The new era of conventional agriculture is represented by precision agriculture. The development of several contemporary technologies, like the internet of things, has made this possible. When relevant, this article emphasizes global and advanced agricultural systems and platforms that utilize IoT technology.<br /><br />Conclusions<br />The effectiveness of such techniques in plant disease detection is proven by their ability to achieve exceptional levels of accuracy. This is particularly true when they rely on extensive open-source databases and pre-trained algorithms. Future investigation uncovered that the size of the plant imagery utilized for modeling and the circumstances under which the photos were gathered could significantly affect the accuracy. <br />. Objective<br />Converting data into digital form has led to a massive influx of data in almost every industry that relies on data-driven operations. The digital data processing has significantly increased the volume of information being processed. The emergence of electronic agriculture management has profoundly impacted Information and Communication Technology (ICT), resulting in advantages for farmers and customers and driving the adoption of technological solutions in rural areas. This study emphasizes the promise of ICT technologies in conventional agriculture and the obstacles to their employment in farming operations.<br /><br />Results<br />This study emphasizes the promise of ICT technologies in conventional agriculture and the obstacles to their employment in farming operations. The research provides thorough information on automation, Internet of Things (IoT) gadgets, and challenges related to Machine Learning (ML). Drones are being contemplated for crop monitoring and production optimization in Precision Agriculture (PA) and Smart Farming (SF). The new era of conventional agriculture is represented by precision agriculture. The development of several contemporary technologies, like the internet of things, has made this possible. When relevant, this article emphasizes global and advanced agricultural systems and platforms that utilize IoT technology.<br /><br />Conclusions<br />The effectiveness of such techniques in plant disease detection is proven by their ability to achieve exceptional levels of accuracy. This is particularly true when they rely on extensive open-source databases and pre-trained algorithms. Future investigation uncovered that the size of the plant imagery utilized for modeling and the circumstances under which the photos were gathered could significantly affect the accuracy. <br />. https://jab.uk.ac.ir/article_4671_722caafb4825ef5d8670710fa29087cf.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 Big genetic data analysis to predict features in cross-breeding to increase food yields Big genetic data analysis to predict features in cross-breeding to increase food yields 237 250 4672 10.22103/jab.2025.24004.1612 FA Priya Vij Department of CS & IT, Kalinga University, Raipur, India. 0009-0005-4629-3413 Patil Manisha Prashant Department of CS &amp; IT, Kalinga University, Raipur, India. 0009-0003-1140-0261 Journal Article 2024 09 06 Objective<br />Plant breeders (PB) have significantly improved agricultural output and quality by utilizing modern scientific and technological developments. Costs have decreased and the PB process has quickened due to the development of genomic tools and sequencing, especially since the human genome project. Addressing global issues pertaining to water resources and food security requires this progress. High-throughput phenotyping, precision agriculture, and crop-scouting have all been improved by the integration of cutting-edge technology such sensor systems, satellite images, robots, big data analytics, and genomics. These developments contribute to the growth of digital agriculture, which has the potential to transform PB by taking a more interdisciplinary approach. To examine the method by which new developments in digital agriculture, genomics, and sensor technologies are changing plant breeding, enhancing crop quality and productivity, and tackling global issues with water resource management and food security.<br /><br />Results<br />Plant breeding has become faster and less expensive due to the combination of genetic tools, sequencing techniques, and contemporary agricultural technologies. Precision agriculture has greatly increased high-throughput phenotyping and crop scouting, by using technology like robotics, big data analytics, and satellite photography. These developments aid in the creation of sustainable, more effective farming methods.<br /><br />Conclusions<br />An innovative approach for crop improvement is being developed by the ongoing integration of multidisciplinary technologies in plant breeding. It is anticipated that enhanced genomics and digital agriculture would improve plant breeders' capacities, allowing them to tackle the escalating problems of food and water security in a world that is becoming more interconnected by the day.<br />. Objective<br />Plant breeders (PB) have significantly improved agricultural output and quality by utilizing modern scientific and technological developments. Costs have decreased and the PB process has quickened due to the development of genomic tools and sequencing, especially since the human genome project. Addressing global issues pertaining to water resources and food security requires this progress. High-throughput phenotyping, precision agriculture, and crop-scouting have all been improved by the integration of cutting-edge technology such sensor systems, satellite images, robots, big data analytics, and genomics. These developments contribute to the growth of digital agriculture, which has the potential to transform PB by taking a more interdisciplinary approach. To examine the method by which new developments in digital agriculture, genomics, and sensor technologies are changing plant breeding, enhancing crop quality and productivity, and tackling global issues with water resource management and food security.<br /><br />Results<br />Plant breeding has become faster and less expensive due to the combination of genetic tools, sequencing techniques, and contemporary agricultural technologies. Precision agriculture has greatly increased high-throughput phenotyping and crop scouting, by using technology like robotics, big data analytics, and satellite photography. These developments aid in the creation of sustainable, more effective farming methods.<br /><br />Conclusions<br />An innovative approach for crop improvement is being developed by the ongoing integration of multidisciplinary technologies in plant breeding. It is anticipated that enhanced genomics and digital agriculture would improve plant breeders' capacities, allowing them to tackle the escalating problems of food and water security in a world that is becoming more interconnected by the day.<br />. https://jab.uk.ac.ir/article_4672_85203ae86f2de2662ca5b6d614fbe495.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 Artificial intelligence approaches for cotton diseases identification: a systematic literature review using biotechnology Artificial intelligence approaches for cotton diseases identification: a systematic literature review using biotechnology 251 264 4673 10.22103/jab.2025.23994.1602 FA Manish Nandy Department of CS & IT, Kalinga University, Raipur, India. 0009-0003-7578-3505 Ahilya Dubey Department of CS &amp; IT, Kalinga University, Raipur, India. 0009-0008-1681-8823 Journal Article 2024 09 06 Objective<br />Cotton is a prominent fiber that commands the worldwide industrial and agricultural sectors. Cotton is a fundamental material used in the creation of textiles. Diagnosing the diseases on cotton plants' leaves soon is essential to prevent them and enhance productivity. Tracking cotton leaf illnesses and assessing plant health is challenging for farmers who rely solely on their subjective expertise and knowledge. Moreover, Artificial neural networks have been proposed to alleviate limitation of traditional methods and can be used to handle nonlinear and complex data, even when the data is imprecise and noisy. Agricultural data can be too large and complex to handle through visual analysis or statistical correlations. This has encouraged the use of machine intelligence or artificial intelligence The objective of this study was to diagnose diseases and improve the cultivation of cotton using Artificial Intelligence (AI) methods.<br /><br />Results<br />The study findings indicate that the current automated detection approaches for cotton crop illnesses are still in their early stages of development with biotechnology and Artificial Intelligence (AI). This review acknowledges the need to develop automated, cost-effective, dependable, precise, and swift diagnostic tools for detecting cotton leaf diseases to enhance output and quality.<br /><br /><br />Conclusions<br />This paper analyzes the several computational techniques used at different phases of plant-pathogen structures, including image preparation, segmentation, extracting features and selecting, and categorization. The study identified valid future paths and areas for additional exploration. There is a need for innovative, fully automated computer-assisted methods to identify and categorize various illnesses in cotton crops.<br />. Objective<br />Cotton is a prominent fiber that commands the worldwide industrial and agricultural sectors. Cotton is a fundamental material used in the creation of textiles. Diagnosing the diseases on cotton plants' leaves soon is essential to prevent them and enhance productivity. Tracking cotton leaf illnesses and assessing plant health is challenging for farmers who rely solely on their subjective expertise and knowledge. Moreover, Artificial neural networks have been proposed to alleviate limitation of traditional methods and can be used to handle nonlinear and complex data, even when the data is imprecise and noisy. Agricultural data can be too large and complex to handle through visual analysis or statistical correlations. This has encouraged the use of machine intelligence or artificial intelligence The objective of this study was to diagnose diseases and improve the cultivation of cotton using Artificial Intelligence (AI) methods.<br /><br />Results<br />The study findings indicate that the current automated detection approaches for cotton crop illnesses are still in their early stages of development with biotechnology and Artificial Intelligence (AI). This review acknowledges the need to develop automated, cost-effective, dependable, precise, and swift diagnostic tools for detecting cotton leaf diseases to enhance output and quality.<br /><br /><br />Conclusions<br />This paper analyzes the several computational techniques used at different phases of plant-pathogen structures, including image preparation, segmentation, extracting features and selecting, and categorization. The study identified valid future paths and areas for additional exploration. There is a need for innovative, fully automated computer-assisted methods to identify and categorize various illnesses in cotton crops.<br />. https://jab.uk.ac.ir/article_4673_cfd66e741860718ddecf1f6eabd05fc6.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 Sustainable and precision agriculture biotechnological model using deep learning algorithm Sustainable and precision agriculture biotechnological model using deep learning algorithm 265 278 4674 10.22103/jab.2025.23998.1606 FA Ashu Nayak Department of CS & IT, Kalinga University, Raipur, India. 0009-0002-8371-7324 Divya Divya Department of CS &amp; IT, Kalinga University, Raipur, India. 0009-0007-2370-3736 Journal Article 2024 09 06 Objective<br />With the help of weather data from the agricultural Internet of Things (IoT) method's, it is possible to plan for changes in the weather. This is an excellent way to prepare and keep track of the production of green agriculture. Thus, the aim of this study was to make weather information forecasting more accurate in the Precision Agriculture (PA) system.<br /><br />Materials and methods<br />It is difficult to accurately predict future trends as the data is complicated and requires simple linear links. The evolution of communication technology and the increasing number of interconnected things have had a profound impact on the agricultural sector. Advances in AI, and deep learning in particular, have facilitated faster and more accurate data processing in this modern digital era. A new data analytics technology called deep learning has the potential to make farming more efficient, eco-friendly, and predictable. In this study Deep Learning (DL) predictions with a two-level decomposition structure and Biotechnology (BT) were used to make the prediction of weather information in the Precision Agriculture (PA) system more accurate. First, the weather data was decomposed into four parts. Then, the Gated Recurrent Unit (GRU) systems were created as sub-predictors for each part. <br /><br /><br /><br />Results<br />First, the weather data was decomposed into four parts. Then, the Gated Recurrent Unit (GRU) systems were created as sub-predictors for each part. The predictions for the medium and long-term future were made by combining the results from the GRUs. Using weather data from the BT-based IoT systems, it was confirmed that the tests work with the suggested structure. <br /><br />Conclusions<br />The proposed prediction method can predict the temperature and humidity correctly and meets the PA standards. It can assist farmers in the management of their agricultural operations. It is possible to provide an initial prediction and assessment of extreme weather conditions in agriculture to minimize risks and maximize profits. Objective<br />With the help of weather data from the agricultural Internet of Things (IoT) method's, it is possible to plan for changes in the weather. This is an excellent way to prepare and keep track of the production of green agriculture. Thus, the aim of this study was to make weather information forecasting more accurate in the Precision Agriculture (PA) system.<br /><br />Materials and methods<br />It is difficult to accurately predict future trends as the data is complicated and requires simple linear links. The evolution of communication technology and the increasing number of interconnected things have had a profound impact on the agricultural sector. Advances in AI, and deep learning in particular, have facilitated faster and more accurate data processing in this modern digital era. A new data analytics technology called deep learning has the potential to make farming more efficient, eco-friendly, and predictable. In this study Deep Learning (DL) predictions with a two-level decomposition structure and Biotechnology (BT) were used to make the prediction of weather information in the Precision Agriculture (PA) system more accurate. First, the weather data was decomposed into four parts. Then, the Gated Recurrent Unit (GRU) systems were created as sub-predictors for each part. <br /><br /><br /><br />Results<br />First, the weather data was decomposed into four parts. Then, the Gated Recurrent Unit (GRU) systems were created as sub-predictors for each part. The predictions for the medium and long-term future were made by combining the results from the GRUs. Using weather data from the BT-based IoT systems, it was confirmed that the tests work with the suggested structure. <br /><br />Conclusions<br />The proposed prediction method can predict the temperature and humidity correctly and meets the PA standards. It can assist farmers in the management of their agricultural operations. It is possible to provide an initial prediction and assessment of extreme weather conditions in agriculture to minimize risks and maximize profits. https://jab.uk.ac.ir/article_4674_1f5795e7b93f423c397e6f7aaff80133.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 Nanotechnology-based biosensors: a typical analysis of materials, methods, and applications Nanotechnology-based biosensors: a typical analysis of materials, methods, and applications 279 292 4675 10.22103/jab.2025.23999.1607 FA Ashu Nayak Department of CS & IT, Kalinga University, Raipur, India. 0009-0002-8371-7324 Vasani Vaibhav Prakash Department of CS &amp; IT, Kalinga University, Raipur, India. 0009-0004-1208-494X Journal Article 2024 09 06 Objective<br />Biosecurity, medical diagnostics, and environmental monitoring are just a few of the many areas that rely on biosensors (BSs). They can identify a wide variety of chemical and biological contaminants, including infectious diseases, pollutants, hazardous materials, and particular biomolecules. Biosensors can detect trace amounts of substances and are easy to use, scalable, inexpensive, and precise. Because of their low cost, user-friendliness, and adaptability, biosensors have become increasingly popular in the medical industry.<br /><br />Results<br />Biosensors have been greatly improved with the incorporation of nanotechnology (NT), especially with regard to detection speed, selectivity, and sensitivity. In NT-based biosensors, nanomaterials (NMs) such nano-wires, Quantum Dots (QDs), Carbon Nanotubes (CNTs), and nano-rods are commonly utilized. Because of their large surface area, NMs improve sensitivity and enable more precise detection of compounds at low concentrations. As a result of their exceptional electrical and thermal conductivity, great carrying capacity, extraordinary durability, and changeable color, they provide an important role in the creation of reliable and efficient biosensors.<br />Conclusions<br />This paper delves into the integration of biosensors that are enabled by nanotechnology, namely NT-enabled BSs. It examines the principles, designs, and materials utilized to make these sensors. There is an emphasis on the expanding role of NT-based biosensors in fields as diverse as healthcare and environmental monitoring. Developing next-generation diagnostic tools, environmental monitoring devices, and biosecurity instruments is made more feasible by combining nanotechnology with biosensors. Due to the special characteristics of nanomaterials, NT-based biosensors could completely change the way we detect substances. They would be able to detect a wide variety of substances much more quickly, accurately, and with a high level of sensitivity, which would be great for research and practical uses in many fields.<br /><br /> Objective<br />Biosecurity, medical diagnostics, and environmental monitoring are just a few of the many areas that rely on biosensors (BSs). They can identify a wide variety of chemical and biological contaminants, including infectious diseases, pollutants, hazardous materials, and particular biomolecules. Biosensors can detect trace amounts of substances and are easy to use, scalable, inexpensive, and precise. Because of their low cost, user-friendliness, and adaptability, biosensors have become increasingly popular in the medical industry.<br /><br />Results<br />Biosensors have been greatly improved with the incorporation of nanotechnology (NT), especially with regard to detection speed, selectivity, and sensitivity. In NT-based biosensors, nanomaterials (NMs) such nano-wires, Quantum Dots (QDs), Carbon Nanotubes (CNTs), and nano-rods are commonly utilized. Because of their large surface area, NMs improve sensitivity and enable more precise detection of compounds at low concentrations. As a result of their exceptional electrical and thermal conductivity, great carrying capacity, extraordinary durability, and changeable color, they provide an important role in the creation of reliable and efficient biosensors.<br />Conclusions<br />This paper delves into the integration of biosensors that are enabled by nanotechnology, namely NT-enabled BSs. It examines the principles, designs, and materials utilized to make these sensors. There is an emphasis on the expanding role of NT-based biosensors in fields as diverse as healthcare and environmental monitoring. Developing next-generation diagnostic tools, environmental monitoring devices, and biosecurity instruments is made more feasible by combining nanotechnology with biosensors. Due to the special characteristics of nanomaterials, NT-based biosensors could completely change the way we detect substances. They would be able to detect a wide variety of substances much more quickly, accurately, and with a high level of sensitivity, which would be great for research and practical uses in many fields.<br /><br /> https://jab.uk.ac.ir/article_4675_6c2e49911b68d315555d5b3eb0dd45bf.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 Biotechnological models for soil health restoration to enhance the soil health and sustainable agriculture: present condition and future research Biotechnological models for soil health restoration to enhance the soil health and sustainable agriculture: present condition and future research 293 306 4676 10.22103/jab.2025.24000.1608 FA Divya Divya Department of CS & IT, Kalinga University, Raipur, India. 0009-0007-2370-3736 Yalakala Dinesh Kumar Department of CS &amp; IT, Kalinga University, Raipur, India. 0009-0001-2126-1969 Journal Article 2024 09 06 Objective<br />Degradation of land quality is a major concern for the United Nations Environment Programme (UNEP) since it harms human health and food security. Restoring healthy soil is a primary goal of sustainable agriculture (SA), as it is essential for crop yields and other ecosystem services. This can be overcome by engineering GMOs to produce materials or enzymes that aid in the breakdown of soil pollutants. In this work, we look at the possibility that GMOs can speed up the bioremediation process and increase efficiency in the removal of toxic contaminants from soil.<br /><br />Materials and methods<br />The study occurred in the Nilokheri part of the Karnal area in Haryana, India. During the field trip, data was collected by interviewing 150 residents in 32 communities of the Nilokheri division in the Karnal area. Information collected included cultivation methods, farming history, varieties utilized, mean yield, thickness, location of irrigation water, and the amount and kind of fertilizers used.<br /><br />Results<br />The research suggests a long-term solution to soil health problems by integrating GMOs, rhizobacteria, and other soil treatments into a single strategy. Within a decade, this integrated approach may have restored soil fertility and reduced pollution impacts, guaranteeing that agricultural land will be sustainable in the long run. Genetically modified organisms (GMOs) have the potential to quicken the land's natural recovery process, which in turn increases the land's production and resistance to persistent environmental threats.<br /><br />Conclusions<br />Finally, this study shows that genetically modified organisms (GMOs) have the potential to improve soil health through better bioremediation, which would lead to more sustainable agriculture. Nevertheless, it also stresses the need to evaluate the advantages of GMOs against their possible ecological dangers and to support a careful, regulated strategy for their use in environmental contexts. Objective<br />Degradation of land quality is a major concern for the United Nations Environment Programme (UNEP) since it harms human health and food security. Restoring healthy soil is a primary goal of sustainable agriculture (SA), as it is essential for crop yields and other ecosystem services. This can be overcome by engineering GMOs to produce materials or enzymes that aid in the breakdown of soil pollutants. In this work, we look at the possibility that GMOs can speed up the bioremediation process and increase efficiency in the removal of toxic contaminants from soil.<br /><br />Materials and methods<br />The study occurred in the Nilokheri part of the Karnal area in Haryana, India. During the field trip, data was collected by interviewing 150 residents in 32 communities of the Nilokheri division in the Karnal area. Information collected included cultivation methods, farming history, varieties utilized, mean yield, thickness, location of irrigation water, and the amount and kind of fertilizers used.<br /><br />Results<br />The research suggests a long-term solution to soil health problems by integrating GMOs, rhizobacteria, and other soil treatments into a single strategy. Within a decade, this integrated approach may have restored soil fertility and reduced pollution impacts, guaranteeing that agricultural land will be sustainable in the long run. Genetically modified organisms (GMOs) have the potential to quicken the land's natural recovery process, which in turn increases the land's production and resistance to persistent environmental threats.<br /><br />Conclusions<br />Finally, this study shows that genetically modified organisms (GMOs) have the potential to improve soil health through better bioremediation, which would lead to more sustainable agriculture. Nevertheless, it also stresses the need to evaluate the advantages of GMOs against their possible ecological dangers and to support a careful, regulated strategy for their use in environmental contexts. https://jab.uk.ac.ir/article_4676_ea6979872125d5acbac6068f186a0359.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 Integration of IoT and biotechnology for real-time crop monitoring and management in smart agriculture Integration of IoT and biotechnology for real-time crop monitoring and management in smart agriculture 307 320 4677 10.22103/jab.2025.24001.1609 FA Aakansha Soy Department of CS & IT, Kalinga University, Raipur, India. 0009-0002-1955-6909 Sutar Manisha Balkrishna Department of CS &amp; IT, Kalinga University, Raipur, India. 0009-0003-8881-3638 Journal Article 2024 09 06 Abstract<br />Objective<br />Modern problems including rising food demand, limited resources, and environmental degradation can be effectively addressed through the revolutionary practice of smart agriculture (SA). Meeting global demand while reducing environmental effect is a challenge for traditional farming practices. By enhancing agricultural methods, increasing crop yields, and decreasing resource consumption, the combination of Biotechnology (BT) with SA provides a revolutionary solution.<br /><br />Material and methods<br />Smart Agriculture systems' incorporation of data analytics and Deep Neural Networks (DNN) has increased the optimization potential of agriculture even further. In order to improve crop management, decrease waste, and increase overall farm production, farmers can use data-informed decisions made possible by DNN algorithms to get practical insights into crop health, growth trends, and ideal farming practices.<br /><br />Results<br />A Real-Time Crop Monitoring and Management (R-CMM) system integrating DNN, Internet of Things (IoT), and Biotechnology (BT) is proposed in this research as an application of Smart Agriculture. By collecting biological signals from the environment using tiny, renewable, and non-invasive sensors, IoBT provides real-time data on plant health, soil conditions, and climate parameters. With this, automated administration of crop systems and continuous monitoring from a distance are both made possible, cutting down on personnel expenses and increasing overall efficiency.<br /><br />Conclusions<br />Indoor crop plantation management relies on a number of critical characteristics, including temperature, humidity, soil moisture, and light intensity, all of which the R-CMM system uses to keep checks on. The platform’s use of DNN algorithms allows for more effective and accurate farming by predicting when crops may experience stress, optimizing the allocation of resources, and detecting early indications of disease or pest infestations.<br /><br /> Abstract<br />Objective<br />Modern problems including rising food demand, limited resources, and environmental degradation can be effectively addressed through the revolutionary practice of smart agriculture (SA). Meeting global demand while reducing environmental effect is a challenge for traditional farming practices. By enhancing agricultural methods, increasing crop yields, and decreasing resource consumption, the combination of Biotechnology (BT) with SA provides a revolutionary solution.<br /><br />Material and methods<br />Smart Agriculture systems' incorporation of data analytics and Deep Neural Networks (DNN) has increased the optimization potential of agriculture even further. In order to improve crop management, decrease waste, and increase overall farm production, farmers can use data-informed decisions made possible by DNN algorithms to get practical insights into crop health, growth trends, and ideal farming practices.<br /><br />Results<br />A Real-Time Crop Monitoring and Management (R-CMM) system integrating DNN, Internet of Things (IoT), and Biotechnology (BT) is proposed in this research as an application of Smart Agriculture. By collecting biological signals from the environment using tiny, renewable, and non-invasive sensors, IoBT provides real-time data on plant health, soil conditions, and climate parameters. With this, automated administration of crop systems and continuous monitoring from a distance are both made possible, cutting down on personnel expenses and increasing overall efficiency.<br /><br />Conclusions<br />Indoor crop plantation management relies on a number of critical characteristics, including temperature, humidity, soil moisture, and light intensity, all of which the R-CMM system uses to keep checks on. The platform’s use of DNN algorithms allows for more effective and accurate farming by predicting when crops may experience stress, optimizing the allocation of resources, and detecting early indications of disease or pest infestations.<br /><br /> https://jab.uk.ac.ir/article_4677_82ba9d6eee3f026be339bb287651c3d8.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 A big data-driven agricultural system for remote biosensing applications A big data-driven agricultural system for remote biosensing applications 321 334 4678 10.22103/jab.2025.23995.1603 FA Debarghya Biswas Department of CS & IT, Kalinga University, Raipur, India. 0009-0004-0730-9948 Ankita Tiwari Department of CS &amp; IT, Kalinga University, Raipur, India. 0009-0007-5517-3848 Journal Article 2024 09 06 Objective<br />This study explores two big data (BD)-driven agricultural models backed by the National Institute of Food and Agriculture (NIFA). It examines the benefits of thorough agricultural records, efficient phenotyping techniques, and teamwork in promoting agriculture, highlighting the role of technological advancements like sensors, robotics, machine learning (ML), big data analytics, remote sensing, and genomics in addressing global food and water security issues. The study's primary goals were to examine the advantages of maintaining accurate agricultural records for better breeding and agronomy, investigate strategies for efficient phenotyping and data collection in agricultural systems, then support interaction between plant breeders, agricultural scientists, and specialists in ML, remote biosensing (RBS), and BD, and to determine the financial requirements for the ongoing advancement of BD-driven agriculture models.<br /><br />Materials and methods<br />The AVIRIS Indian Pines database was utilized in these tests. The Indian Pines database encompasses the farming industry. The dataset consists of 16 different groups. The studies used an Intel i5 laptop with a 2.4-GHz Central Processing Unit (CPU) (four cores) and 16 gigabytes of Memory.<br /><br />Results<br />The integration of technology, including sensors, remote sensing, robots, and BD analytics, enhances high-throughput phenotyping and precision farming. Multidisciplinary cooperation accelerates crop breeding and management. Future financing is needed for predictive machine learning models and scalable phenotyping techniques.<br /><br />Conclusions<br />BD analytics, remote sensing, and machine learning have the ability to transform agronomy and plant breeding, tackling issues related to food security. Sustained cooperation and sufficient infrastructure investment are essential for successful implementation, with specific funding required for the development of cutting-edge instruments and technologies that guarantee sustainable farming methods.<br />. Objective<br />This study explores two big data (BD)-driven agricultural models backed by the National Institute of Food and Agriculture (NIFA). It examines the benefits of thorough agricultural records, efficient phenotyping techniques, and teamwork in promoting agriculture, highlighting the role of technological advancements like sensors, robotics, machine learning (ML), big data analytics, remote sensing, and genomics in addressing global food and water security issues. The study's primary goals were to examine the advantages of maintaining accurate agricultural records for better breeding and agronomy, investigate strategies for efficient phenotyping and data collection in agricultural systems, then support interaction between plant breeders, agricultural scientists, and specialists in ML, remote biosensing (RBS), and BD, and to determine the financial requirements for the ongoing advancement of BD-driven agriculture models.<br /><br />Materials and methods<br />The AVIRIS Indian Pines database was utilized in these tests. The Indian Pines database encompasses the farming industry. The dataset consists of 16 different groups. The studies used an Intel i5 laptop with a 2.4-GHz Central Processing Unit (CPU) (four cores) and 16 gigabytes of Memory.<br /><br />Results<br />The integration of technology, including sensors, remote sensing, robots, and BD analytics, enhances high-throughput phenotyping and precision farming. Multidisciplinary cooperation accelerates crop breeding and management. Future financing is needed for predictive machine learning models and scalable phenotyping techniques.<br /><br />Conclusions<br />BD analytics, remote sensing, and machine learning have the ability to transform agronomy and plant breeding, tackling issues related to food security. Sustained cooperation and sufficient infrastructure investment are essential for successful implementation, with specific funding required for the development of cutting-edge instruments and technologies that guarantee sustainable farming methods.<br />. https://jab.uk.ac.ir/article_4678_ac4d17530106c3e3c2fb5e2dad0e51b7.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 Using agricultural big data analytics in plant breeding and genetics to increase food yield Using agricultural big data analytics in plant breeding and genetics to increase food yield 335 348 4679 10.22103/jab.2025.24002.1610 FA Ashu Nayak Department of CS & IT, Kalinga University, Raipur, India. 0009-0002-8371-7324 Kapesh Subhash Raghatate Department of CS &amp; IT, Kalinga University, Raipur, India. 0009-0007-9036-1983 Journal Article 2024 09 06 Objective<br />When it comes to a healthy economy and population, the agriculture sector is essential. Smart Agriculture (SA) is a game-changing strategy that optimizes agricultural techniques with the use of cutting-edge technology like Big Data Analytics and the Internet of Things (IoT), in response to the rising need for food on a worldwide scale. The Internet of Things (IoT) gathers massive quantities of data from farms, allowing for more accurate disease control, irrigation methods, and crop output predictions. The goal of this research is to predict and improve grape plant production using an N-stage Convolutional Neural Network (CNN) trained using data from the SA database.<br /><br />Materials and Methods<br />Optimal irrigation scheduling and amount prediction methods are also implemented in the research via the use of Machine Learning approaches. One useful method for early detection and treatment of plant illnesses is being investigated in this research: a Double Generative Adversarial Network (DGAN). This network might be used by farmers.<br /><br />Results<br />The primary goal of this study is to develop a multi-stage convolutional neural network (CNN) model that can considerably boost agricultural output, with a focus on grape production. <br /><br /><br />Conclusions<br />A comprehensive strategy for grape plant development management is offered by the model via the integration of critical characteristics such as irrigation scheduling and disease diagnosis. Farmers are able to maximize their resources and output with the aid of this method, which also enhances the accuracy of yield predictions and facilitates better management decisions. In order to increase food production on a worldwide scale and promote sustainable agricultural techniques, this study's findings may lead to the wider use of Smart Agriculture methods.<br /><br /> Objective<br />When it comes to a healthy economy and population, the agriculture sector is essential. Smart Agriculture (SA) is a game-changing strategy that optimizes agricultural techniques with the use of cutting-edge technology like Big Data Analytics and the Internet of Things (IoT), in response to the rising need for food on a worldwide scale. The Internet of Things (IoT) gathers massive quantities of data from farms, allowing for more accurate disease control, irrigation methods, and crop output predictions. The goal of this research is to predict and improve grape plant production using an N-stage Convolutional Neural Network (CNN) trained using data from the SA database.<br /><br />Materials and Methods<br />Optimal irrigation scheduling and amount prediction methods are also implemented in the research via the use of Machine Learning approaches. One useful method for early detection and treatment of plant illnesses is being investigated in this research: a Double Generative Adversarial Network (DGAN). This network might be used by farmers.<br /><br />Results<br />The primary goal of this study is to develop a multi-stage convolutional neural network (CNN) model that can considerably boost agricultural output, with a focus on grape production. <br /><br /><br />Conclusions<br />A comprehensive strategy for grape plant development management is offered by the model via the integration of critical characteristics such as irrigation scheduling and disease diagnosis. Farmers are able to maximize their resources and output with the aid of this method, which also enhances the accuracy of yield predictions and facilitates better management decisions. In order to increase food production on a worldwide scale and promote sustainable agricultural techniques, this study's findings may lead to the wider use of Smart Agriculture methods.<br /><br /> https://jab.uk.ac.ir/article_4679_97788494d0cb9c4ad37af9a76290b361.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 The integration of artificial intelligence (AI) and high-throughput phenotyping (HTP) to estimate agricultural traits in crop development The integration of artificial intelligence (AI) and high-throughput phenotyping (HTP) to estimate agricultural traits in crop development 349 362 4680 10.22103/jab.2025.24005.1613 FA Kamlesh Kumar Yadav Department of CS & IT, Kalinga University, Raipur, India. 0009-0006-4665-5506 Balasubramaniam Kumaraswamy Department of CS & IT, Kalinga University, Raipur, India. Journal Article 2024 09 06 Objective<br />The growing demand for food throughout the world is a serious problem that requires creative agricultural solutions to guarantee food security and sustainable farming methods. Artificial Intelligence (AI) and High-Throughput Phenotyping (HTP) are two new technologies that allow for the quick and accurate measurement and analysis of agricultural characteristics, allowing for the discovery of critical elements influencing quality and growth. HTP uses cutting-edge sensors, imaging, and other technologies to gather enormous databases on plant characteristics.<br /><br />Materials and methods<br />Finding underlying patterns and correlations between phenotypic features and genetic data has become easier because to the combination of HTP with AI and Machine Learning (ML) algorithms. These large datasets may be processed effectively by AI-driven algorithms, which speeds up the process of identifying desired crop features for breeding initiatives.<br /><br />Results<br />Predictive technologies that support data-driven decision-making in crop breeding have been made possible by the combination of HTP, AI, and ML. By increasing accuracy and speeding up the breeding process, these instruments help raise agricultural production and sustainability. However, issues including data complexity, established procedures, and ongoing advancements in computational models still stand in the way of completely integrating these technologies throughout agricultural systems. For AI and HTP technologies to be successfully implemented on a broader scale, cooperation between researchers, industry, and farmers is also required.<br /><br />Conclusions<br />The benefits of these technologies, such as improved efficiency and accuracy in selecting ideal breeding characteristics, are examined in this study as it investigates the safe and efficient integration of HTP and AI to improve crop growth and quality. It shows the latest progress and real-world uses of HTP and AI in farming, showing how these new technologies have already started to change the way crops are cultivated. Objective<br />The growing demand for food throughout the world is a serious problem that requires creative agricultural solutions to guarantee food security and sustainable farming methods. Artificial Intelligence (AI) and High-Throughput Phenotyping (HTP) are two new technologies that allow for the quick and accurate measurement and analysis of agricultural characteristics, allowing for the discovery of critical elements influencing quality and growth. HTP uses cutting-edge sensors, imaging, and other technologies to gather enormous databases on plant characteristics.<br /><br />Materials and methods<br />Finding underlying patterns and correlations between phenotypic features and genetic data has become easier because to the combination of HTP with AI and Machine Learning (ML) algorithms. These large datasets may be processed effectively by AI-driven algorithms, which speeds up the process of identifying desired crop features for breeding initiatives.<br /><br />Results<br />Predictive technologies that support data-driven decision-making in crop breeding have been made possible by the combination of HTP, AI, and ML. By increasing accuracy and speeding up the breeding process, these instruments help raise agricultural production and sustainability. However, issues including data complexity, established procedures, and ongoing advancements in computational models still stand in the way of completely integrating these technologies throughout agricultural systems. For AI and HTP technologies to be successfully implemented on a broader scale, cooperation between researchers, industry, and farmers is also required.<br /><br />Conclusions<br />The benefits of these technologies, such as improved efficiency and accuracy in selecting ideal breeding characteristics, are examined in this study as it investigates the safe and efficient integration of HTP and AI to improve crop growth and quality. It shows the latest progress and real-world uses of HTP and AI in farming, showing how these new technologies have already started to change the way crops are cultivated. https://jab.uk.ac.ir/article_4680_4f5c422f4d49a5a807eda27434231040.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 The effect of biological soil disinfestation on dry rot disease in industrial potatoes caused by Fusarium oxysporum The effect of biological soil disinfestation on dry rot disease in industrial potatoes caused by Fusarium oxysporum 363 376 4681 10.22103/jab.2024.24371.1629 FA Maab Hashi Deptartment of Plant Protection, College of Agriculture and Engineering Science, University of Baghdad, Iraq 0009-0009-5251-6547 Tariq Kareem Deptartment of Plant Protection, College of Agriculture and Engineering Science, University of Baghdad, Iraq 0000-0002-6238-9723 Journal Article 2024 11 13 Objective<br />Biological soil disinfestation methods have been employed as an alternative to methyl bromide, as they are effective in eliminating soil-borne pathogens, environmentally safe, and cost-effective. The aim of this study was to evaluate the efficiency of the biological disinfestation method using three local organic materials in controlling the pathogenic fungus Fusarium oxysporum on industrial potato crops.<br />Materials and methods<br />Industrial potato tubers were collected from various potato-growing regions in Baghdad Province. Three tubers were used for each fungal isolate. The inoculated tubers were stored in an incubator at 15°C with 70–85% humidity for 30 days. This study identified 30 isolates of Fusarium spp. from infected potato tubers and roots, showing significant variation in growth rates, colony colors, and mycelia density. The severity of infection was calculated based on a scale related to root weight, and various growth parameters were measured, including the number of branches, branch length, number of tubers, tuber weight, and the fresh and dry weight of the root system. The experiment was conducted using a randomized complete block design (RCBD).<br />Results<br />Pathogenicity tests on potato buds revealed that isolates F1, F5, F8, F10, F15, F20, F21, F28, and F30 caused the highest infection severity, ranging from 75% to 100%. Similarly, the tests on potato tubers confirmed that all isolates could induce dry rot, with damaged tissue areas ranging from 39.16 to 59.64 mm², significantly differing from the control treatment. In Biological disinfestation tests, wheat bran at concentrations of 100, 200, and 300 g/m² significantly reduced infection severity, ranging from 5% to 26%, compared to the 100% infection in the pathogenic fungus control. Sawdust also showed significant reductions, with infection severity between 46% and 60%, while corn husks reduced infection severity to a range between 60% and 80%. Wheat bran demonstrated superior performance in tuber weight, producing tuber weights of 282 g, 362.10 g, and 469.53 g per plant, significantly higher than the control (150 g per plant). In additional biological disinfestation tests, wheat bran reduced infection severity by 25% to 50%, sawdust by 50% to 75%, and corn husks by 66% to 91%, compared to the control with 100% infection.<br />Conclusions<br />Tuber weights for these treatments were also significantly higher than the control, with wheat bran showing the best results. Overall, biological soil disinfestation treatments, especially wheat bran, are promising methods for controlling Fusarium infections and improving potato tuber yield. Objective<br />Biological soil disinfestation methods have been employed as an alternative to methyl bromide, as they are effective in eliminating soil-borne pathogens, environmentally safe, and cost-effective. The aim of this study was to evaluate the efficiency of the biological disinfestation method using three local organic materials in controlling the pathogenic fungus Fusarium oxysporum on industrial potato crops.<br />Materials and methods<br />Industrial potato tubers were collected from various potato-growing regions in Baghdad Province. Three tubers were used for each fungal isolate. The inoculated tubers were stored in an incubator at 15°C with 70–85% humidity for 30 days. This study identified 30 isolates of Fusarium spp. from infected potato tubers and roots, showing significant variation in growth rates, colony colors, and mycelia density. The severity of infection was calculated based on a scale related to root weight, and various growth parameters were measured, including the number of branches, branch length, number of tubers, tuber weight, and the fresh and dry weight of the root system. The experiment was conducted using a randomized complete block design (RCBD).<br />Results<br />Pathogenicity tests on potato buds revealed that isolates F1, F5, F8, F10, F15, F20, F21, F28, and F30 caused the highest infection severity, ranging from 75% to 100%. Similarly, the tests on potato tubers confirmed that all isolates could induce dry rot, with damaged tissue areas ranging from 39.16 to 59.64 mm², significantly differing from the control treatment. In Biological disinfestation tests, wheat bran at concentrations of 100, 200, and 300 g/m² significantly reduced infection severity, ranging from 5% to 26%, compared to the 100% infection in the pathogenic fungus control. Sawdust also showed significant reductions, with infection severity between 46% and 60%, while corn husks reduced infection severity to a range between 60% and 80%. Wheat bran demonstrated superior performance in tuber weight, producing tuber weights of 282 g, 362.10 g, and 469.53 g per plant, significantly higher than the control (150 g per plant). In additional biological disinfestation tests, wheat bran reduced infection severity by 25% to 50%, sawdust by 50% to 75%, and corn husks by 66% to 91%, compared to the control with 100% infection.<br />Conclusions<br />Tuber weights for these treatments were also significantly higher than the control, with wheat bran showing the best results. Overall, biological soil disinfestation treatments, especially wheat bran, are promising methods for controlling Fusarium infections and improving potato tuber yield. https://jab.uk.ac.ir/article_4681_d26beb4d23d4930fba836087f83d9bcf.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 Exploring the role of Alk, PAH and CYP153 genes in removing of bitumen contaminants Exploring the role of Alk, PAH and CYP153 genes in removing of bitumen contaminants 377 390 4682 10.22103/jab.2024.24493.1634 FA Dhuha Majid Hassouni National Center for Laboratories and Construction Research, Wasit Construction Laboratory, AL-Kut, Wasit, Iraq. 0009-0009-8145-3557 Melad Khalaf Mohammed Department of Biology, College of Science, Wasit University, Iraq. 0009-0009-5873-5417 Journal Article 2024 12 11 Objective<br />Alkane hydroxylase is an enzyme involved in the first stage of alkane degradation the alkB gene is important for the biodegradation of bitumen because bitumen contains a lot of hydrocarbons such as alkanes, bacteria with the is alkane monooxygenase (AlkB) gene can break it down. The regulatory mechanisms can be intricate and species-specific; there are enzymes encoded by the PAH gene that initiate the breakdown of polycyclic aromatic hydrocarbons (PAHs); and PAHs are dangerous pollutants that can be detected in water and soil. An alkane hydroxylase, specifically a cytochrome P450 enzyme, is encoded by the cytochrome P450 Class I P450 (CYP153) gene. this enzyme is essential for the biodegradation of hydrocarbons, which includes bitumen. Enzymes belonging to the cytochrome P450 family play an important role in the metabolism of many different compounds, including those that are foreign to the body. One of bitumen's main components, alkanes, can be oxidized by the enzyme CYP153. Thus, the aim of this study was to explore the role of Alk, PAH and CYP153 genes in removing of bitumen contaminants.<br /><br />Materials and Methods<br />Genomic DNA was extracted using the standard DNA extraction Kit. The quality and quantity of extracted DNA were determined using nanodrop device. The specific primers were used to amplify AlkB, PAHs and Cyp153 genes. Visualization of the amplified fragments was performed using a transilluminator under ultraviolet light and photographed. <br /><br />Results <br />Extracted DNA had good quality and quantity (10ng/μL). Alkane monooxygenase (alkB), PAH, and CYP153 are three key enzyme-encoding genes that play an essential role in the mineralization of aliphatic and PAH chemicals, respectively. The presence of these three genes (alkB, PAH, and CYP153) was detected based on PCR amplification and visualized on agarose gel. Frequency of bitumen utilization genes was different. It was the highest for CYP152 gene and the lowest for AlkB gene.<br /><br />Conclusions<br />The results highlight the potential for bioremediation applications, especially in bitumen-contaminated areas of employing native bacteria such as Pseudomonas aeruginosa to validate these bacteria's effectiveness in practical settings and create scalable bioremediation techniques for reducing hydrocarbon contamination field research is necessary. Objective<br />Alkane hydroxylase is an enzyme involved in the first stage of alkane degradation the alkB gene is important for the biodegradation of bitumen because bitumen contains a lot of hydrocarbons such as alkanes, bacteria with the is alkane monooxygenase (AlkB) gene can break it down. The regulatory mechanisms can be intricate and species-specific; there are enzymes encoded by the PAH gene that initiate the breakdown of polycyclic aromatic hydrocarbons (PAHs); and PAHs are dangerous pollutants that can be detected in water and soil. An alkane hydroxylase, specifically a cytochrome P450 enzyme, is encoded by the cytochrome P450 Class I P450 (CYP153) gene. this enzyme is essential for the biodegradation of hydrocarbons, which includes bitumen. Enzymes belonging to the cytochrome P450 family play an important role in the metabolism of many different compounds, including those that are foreign to the body. One of bitumen's main components, alkanes, can be oxidized by the enzyme CYP153. Thus, the aim of this study was to explore the role of Alk, PAH and CYP153 genes in removing of bitumen contaminants.<br /><br />Materials and Methods<br />Genomic DNA was extracted using the standard DNA extraction Kit. The quality and quantity of extracted DNA were determined using nanodrop device. The specific primers were used to amplify AlkB, PAHs and Cyp153 genes. Visualization of the amplified fragments was performed using a transilluminator under ultraviolet light and photographed. <br /><br />Results <br />Extracted DNA had good quality and quantity (10ng/μL). Alkane monooxygenase (alkB), PAH, and CYP153 are three key enzyme-encoding genes that play an essential role in the mineralization of aliphatic and PAH chemicals, respectively. The presence of these three genes (alkB, PAH, and CYP153) was detected based on PCR amplification and visualized on agarose gel. Frequency of bitumen utilization genes was different. It was the highest for CYP152 gene and the lowest for AlkB gene.<br /><br />Conclusions<br />The results highlight the potential for bioremediation applications, especially in bitumen-contaminated areas of employing native bacteria such as Pseudomonas aeruginosa to validate these bacteria's effectiveness in practical settings and create scalable bioremediation techniques for reducing hydrocarbon contamination field research is necessary. https://jab.uk.ac.ir/article_4682_75ebb02f92fc30a8040bbd625af999f1.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 Production of keratinase enzyme from a local isolate of Kocuria rosea using environmental waste Production of keratinase enzyme from a local isolate of Kocuria rosea using environmental waste 391 412 4683 10.22103/jab.2025.24585.1642 FA Ahmed Abdulameer MSc Student, Department of Biology, College of Education for Pure Sciences, University of Anbar, Anbar, Iraq. 0009-0009-7644-3688 Dhafer Fakhri Al-Rawi Professor, Department of Biology, College of Education for Pure Sciences, University of Anbar, Anbar, Iraq. 0000-0002-9680-3996 Mohammed Qais Al-Ani Professor, Department of Biology, College of Education for Pure Sciences, University of Anbar, Anbar, Iraq. 0000-0003-0868-2280 Journal Article 2024 12 26 Objective<br />Keratinase is an enzyme that belongs to the metalloprotease group, along with many other protein-degrading enzymes. Keratinase is a specialized enzyme that acts on the substrate (keratin). It breaks the strong chemical bonds of keratin. This study aimed to produce the enzyme keratinase using local bacterial isolates obtained from soil samples and poultry waste from different areas in Al-Anbar province, using various waste materials such as hooves, horns, and hides. <br /><br />Materials and methods<br />Seventeen bacterial isolates were obtained, which demonstrated a high ability to grow on feather agar medium used for isolation, from 20 soil and poultry waste samples. The five most efficient bacterial isolates were selected based on the diameter of the colonies growing on the feather agar medium. Among them, the bacterial isolate designated with the local code A2 was chosen as the most efficient in keratin degradation after culturing the five selected isolates on pure keratin medium. The bacterial isolate was identified based on morphological, cultural, microscopic characteristics, and biochemical tests. The VITEK 2 Compact device was used to confirm the identification.<br /><br />Results<br />The results indicated that the isolate was Kocuria rosea. The optimal conditions for enzyme production from Kocuria rosea, including temperature, pH, inoculum size, carbon source and its concentration, nitrogen source and its concentration, and incubation time were studied. Based on the experiments and their results, the medium prepared using sheep hooves was selected for the growth of Kocuria rosea and the production of keratinase, particularly since it is an environmentally available and inexpensive waste in the local environment. The results of the optimal conditions study showed that the best production of keratinase enzyme was at a temperature of 30°C, a pH of 8.0, shaking speed of 150 rpm, with 3g/100mL of sheep hooves in the medium, 5mL/100mL inoculum, using urea as the nitrogen source at a concentration of 0.1g/100mL, and an incubation time of 72 hours. The enzymatic activity reached 0.301 U/mL.<br /><br />Conclusions<br />These findings underscore the potential of Kocuria rosea in bioindustrial applications, particularly in processes involving keratin waste recycling and sustainable waste management. Objective<br />Keratinase is an enzyme that belongs to the metalloprotease group, along with many other protein-degrading enzymes. Keratinase is a specialized enzyme that acts on the substrate (keratin). It breaks the strong chemical bonds of keratin. This study aimed to produce the enzyme keratinase using local bacterial isolates obtained from soil samples and poultry waste from different areas in Al-Anbar province, using various waste materials such as hooves, horns, and hides. <br /><br />Materials and methods<br />Seventeen bacterial isolates were obtained, which demonstrated a high ability to grow on feather agar medium used for isolation, from 20 soil and poultry waste samples. The five most efficient bacterial isolates were selected based on the diameter of the colonies growing on the feather agar medium. Among them, the bacterial isolate designated with the local code A2 was chosen as the most efficient in keratin degradation after culturing the five selected isolates on pure keratin medium. The bacterial isolate was identified based on morphological, cultural, microscopic characteristics, and biochemical tests. The VITEK 2 Compact device was used to confirm the identification.<br /><br />Results<br />The results indicated that the isolate was Kocuria rosea. The optimal conditions for enzyme production from Kocuria rosea, including temperature, pH, inoculum size, carbon source and its concentration, nitrogen source and its concentration, and incubation time were studied. Based on the experiments and their results, the medium prepared using sheep hooves was selected for the growth of Kocuria rosea and the production of keratinase, particularly since it is an environmentally available and inexpensive waste in the local environment. The results of the optimal conditions study showed that the best production of keratinase enzyme was at a temperature of 30°C, a pH of 8.0, shaking speed of 150 rpm, with 3g/100mL of sheep hooves in the medium, 5mL/100mL inoculum, using urea as the nitrogen source at a concentration of 0.1g/100mL, and an incubation time of 72 hours. The enzymatic activity reached 0.301 U/mL.<br /><br />Conclusions<br />These findings underscore the potential of Kocuria rosea in bioindustrial applications, particularly in processes involving keratin waste recycling and sustainable waste management. https://jab.uk.ac.ir/article_4683_d1e96978c6935ec01d995b1b8e4d8c33.pdf

Shahid Bahonar University of Kerman and Iranian Biotechnology Society Agricultural Biotechnology Journal 2228-6705 16 4 2024 12 21 The role and diverse applications of machine learning in genetics, breeding, and biotechnology of livestock and poultry The role and diverse applications of machine learning in genetics, breeding, and biotechnology of livestock and poultry 413 442 4684 10.22103/jab.2025.24662.1644 FA Mohammadreza Mohammadabadi Professor of Animal Science Department, Shahid Bahonar University of Kerman 0000-0002-1268-3043 Alireza Akhtarpoor Department of Biology, Faculty of Science, Shahid Bahonar University of Kerman, Kerman, Iran 0009-0008-0998-3644 Amin Khezri Animal Science Department, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran. 0000-0002-5371-9831 Olena Babenko Department of Animal Science, Bila Tserkva National Agrarian University, Bila Tserkva, Ukraine. 0000-0002-8404-3272 Ruslana Volodymyrivna Stavetska Department of Animal Science, Bila Tserkva National Agrarian University, Bila Tserkva, Ukraine. 0000-0003-0149-1908 Iryna Tytarenko Department of Animal Science, Bila Tserkva National Agrarian University, Bila Tserkva, Ukraine. 0000-0001-6703-2232 Yulia Ievstafiieva Department of Technologies of Livestock Production and processing, Higher Educational Institution “Podillia State University”, Ukraine. 0000-0001-5914-893X Vita Buchkovska Department of Technologies of Livestock Production and processing, Higher Educational Institution “Podillia State University”, Ukraine. 0000-0002-6574-8840 Viktor Slynko Poltava State Agrarian University, Ukraine. 0000-0002-1673-5840 Volodymyr Afanasenko National University of Life and Environmental Sciences of Ukraine, Ukraine. 0000-0002-2782-5403 Journal Article 2025 01 11 Objective<br />Machine learning is a subset of artificial intelligence that is uniquely suited to address challenges in the fields of genetics, breeding, and biotechnology of livestock and poultry. By using algorithms that can learn patterns from data, machine learning enables accurate predictions, automated decision-making, and innovative solutions to complex problems in animal science. Unlike traditional statistical methods, which often assume linearity and independence among variables, machine learning is able to capture nonlinear relationships and interactions between genomic, environmental, and phenotypic factors. Therefore, the purpose of this study was to review the common types of machine learning algorithms used in livestock and poultry breeding, to outline their advantages and disadvantages, and to provide practical examples for these algorithms in the fields of genetics, breeding, and biotechnology of livestock and poultry. <br />Materials and Methods<br />In this study, by reviewing relevant databases and journals, studies related to machine learning in the field of genetics and breeding and biotechnology of livestock and poultry were searched using keywords. These studies were evaluated based on their design, methodology, results and relevance, and the main findings and concepts were extracted from them. <br />Results<br />The results showed that machine learning methods significantly outperform conventional methods. So that machine learning methods improve prediction accuracy and have smaller mean square error (MSE) and mean absolute error (MAE) in all scenarios. The findings also show the potential of combining classical bioinformatics methods with machine learning techniques to improve genomic prediction in the future. The results suggest machine learning algorithms as a promising tool to improve decision-making for livestock farmers. Machine learning analysis improves monitoring methods and allows livestock farmers to identify animals that are likely to have problems in the future. <br />Conclusions<br />This study shows that the use of machine learning methods in the field of genetics, breeding, and biotechnology of livestock and poultry is increasing, and with this increase, the quality of machine learning methods used is also improving. Therefore, machine learning can play an important and prominent role in the sustainable development of livestock farming and provide benefits such as increased productivity in this field. Therefore, this study recommends that the use of machine learning methods and algorithms be promoted among the activists in the field of genetics, breeding, and biotechnology of livestock and poultry to identify and predict problems earlier and more accurately and prevent problems and economic losses. Objective<br />Machine learning is a subset of artificial intelligence that is uniquely suited to address challenges in the fields of genetics, breeding, and biotechnology of livestock and poultry. By using algorithms that can learn patterns from data, machine learning enables accurate predictions, automated decision-making, and innovative solutions to complex problems in animal science. Unlike traditional statistical methods, which often assume linearity and independence among variables, machine learning is able to capture nonlinear relationships and interactions between genomic, environmental, and phenotypic factors. Therefore, the purpose of this study was to review the common types of machine learning algorithms used in livestock and poultry breeding, to outline their advantages and disadvantages, and to provide practical examples for these algorithms in the fields of genetics, breeding, and biotechnology of livestock and poultry. <br />Materials and Methods<br />In this study, by reviewing relevant databases and journals, studies related to machine learning in the field of genetics and breeding and biotechnology of livestock and poultry were searched using keywords. These studies were evaluated based on their design, methodology, results and relevance, and the main findings and concepts were extracted from them. <br />Results<br />The results showed that machine learning methods significantly outperform conventional methods. So that machine learning methods improve prediction accuracy and have smaller mean square error (MSE) and mean absolute error (MAE) in all scenarios. The findings also show the potential of combining classical bioinformatics methods with machine learning techniques to improve genomic prediction in the future. The results suggest machine learning algorithms as a promising tool to improve decision-making for livestock farmers. Machine learning analysis improves monitoring methods and allows livestock farmers to identify animals that are likely to have problems in the future. <br />Conclusions<br />This study shows that the use of machine learning methods in the field of genetics, breeding, and biotechnology of livestock and poultry is increasing, and with this increase, the quality of machine learning methods used is also improving. Therefore, machine learning can play an important and prominent role in the sustainable development of livestock farming and provide benefits such as increased productivity in this field. Therefore, this study recommends that the use of machine learning methods and algorithms be promoted among the activists in the field of genetics, breeding, and biotechnology of livestock and poultry to identify and predict problems earlier and more accurately and prevent problems and economic losses. https://jab.uk.ac.ir/article_4684_4aaa76178f8567e05c8e8295c96171d8.pdf