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