The integration of artificial intelligence (AI) and high-throughput phenotyping (HTP) to estimate agricultural traits in crop development

Document Type : Research Paper

Authors

1 Department of CS & IT, Kalinga University, Raipur, India.

2 Department of CS & IT, Kalinga University, Raipur, India.

10.22103/jab.2025.24005.1613

Abstract

Objective
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.

Materials and methods
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.

Results
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.

Conclusions
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.

Keywords


Angin P, Anisi MH, Göksel F, et al. (2020) Agrilora: a digital twin framework for smart agriculture. J Wireless Mobile Netw Ubiquit Comput Depend Appl 11(4), 77-96.
Bergsträsser S, Fanourakis D, Schmittgen S, et al. (2015) HyperART: non-invasive quantification of leaf traits using hyperspectral absorption-reflectance-transmittance imaging. Plant Methods 11, 1-17.
Camgözlü Y, Kutlu Y (2023) Leaf Image Classification Based on Pre-trained Convolutional Neural Network Models. Nat Eng Sci 8(3), 214-232.
Cho JS, Shrestha S, Kagiyama N, et al. (2020) A network-based “phenomics” approach for discovering patient subtypes from high-throughput cardiac imaging data. JACC Cardiovasc Imaging 13(8), 1655-1670.
Feng L, Chen S, Zhang C, et al. (2021) A comprehensive review on recent applications of unmanned aerial vehicle remote sensing with various sensors for high-throughput plant phenotyping. Comput Electron Agric 182, 106033.
Ghotbaldini H, Mohammadabadi MR, Nezamabadi-pour H, et al. (2019) Predicting breeding value of body weight at 6-month age using Artificial Neural Networks in Kermani sheep breed. Acta Scientiarum Anim Sci 41, e45282.
Gill T, Gill SK, Saini DK, et al. (2022) A comprehensive review of high throughput phenotyping and machine learning for plant stress phenotyping. Phenomics 2(3), 156-183.
Granier C, Aguirrezabal L, Chenu K, et al. (2006) PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit. New Phytol 169(3), 623-635.
Jangra S, Chaudhary V, Yadav RC, Yadav NR (2021) High-throughput phenotyping: a platform to accelerate crop improvement. Phenomics 1(2), 31-53.
Jiang Y, Li C (2020) Convolutional neural networks for image-based high-throughput plant phenotyping: a review. Plant Phenomics 2020, e4152816.
Khan N, Ray RL, Sargani GR, et al. (2021) Current progress and future prospects of agriculture technology: Gateway to sustainable agriculture. Sustainability 13(9), e4883.
Kim JY (2020) Roadmap to high throughput phenotyping for plant breeding. J Biosyst Eng 45, 43-55.
Koh JC, Spangenberg G, Kant S (2021) Automated machine learning for high-throughput image-based plant phenotyping. Remote Sens 13(5), e858.
Lube V, Noyan MA, Przybysz A, et al. (2022) MultipleXLab: A high-throughput portable live-imaging root phenotyping platform using deep learning and computer vision. Plant Methods 18(1), e38.
Mohammadabadi M, Kheyrodin H, Afanasenko V, et al. (2024) The role of artificial intelligence in genomics. Agric Biotechnol J 16 (2), 195-279.
Mumtaj Begum H (2022) Scientometric Analysis of the Research Paper Output on Artificial Intelligence: A Study. Indian J Inform Source Serv 12(1), 52–58.
Pour Hamidi S, Mohammadabadi MR, Asadi Foozi M, Nezamabadi-pour H (2017) Prediction of breeding values for the milk production trait in Iranian Holstein cows applying artificial neural networks. J Livestock Sci Technol 5 (2), 53-61.
Pržulj N, Tunguz V, Jovović Z, Velimirović A (2022) The Significance of Harvest residues in the Sustainable Management of Arable Land. II. Harvest Residues Management. Arch Tech Sci 2(27), 49-56.
Surendar A, Saravanakumar V, Sindhu S, Arvinth N (2024) A Bibliometric study of publication-citations in a range of journal articles. Indian J Inf Sour Serv 14(2), 97-103.
Veerasamy K, Fredrik ET (2023) Intelligent Farming based on Uncertainty Expert System with Butterfly Optimization Algorithm for Crop Recommendation. J Internet Serv Inf Secur 13(3), 158-169.
Xiao Q, Bai X, Zhang C, He Y (2022) Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review. J Adv Res 35, 215-230.
Yang W, Feng H, Zhang X, et al. (2020) Crop phenomics and high-throughput phenotyping: past decades, current challenges, and future perspectives. Mol Plant 13(2), 187-214.
Zhu F, Saluja M, Dharni JS, et al. (2021) Pheno Image: An open‐source graphical user interface for plant image analysis. Plant Phenom J 4(1), e20015.