AI in Agriculture: Techniques and Applications
Article Sidebar
Main Article Content
Abstract
The role of agriculture in providing food security globally cannot be overstated, but it is associated with various complex issues, and agricultural researchers have found that Machine Learning (ML), as a subset of Artificial Intelligence (AI), is helpful in precision decision-making through learning from multiple agricultural data types. This paper reviews various state-of-the-art ML algorithms applied to crop yield, disease identification, soil/water management, and decision support systems. The paper summarizes various ML models, applications, and trends, as well as research challenges and gaps, aiming to offer insights to guide future research and applications.
Article Details
Issue
Section
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
References
Food and Agriculture Organization of the United Nations, The Future of Food and Agriculture – Trends and Challenges, FAO, Rome, 2017. [Online]. Available: https://www.fao.org
A. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Computers and Electronics in Agriculture, vol. 147, pp. 70–90, 2018. doi: 10.1016/j.compag.2018.02.016
K. G. Liakos, P. Busato, D. Moshou, S. Pearson, and D. Bochtis, “Machine learning in agriculture: A review,” Sensors, vol. 18, no. 8, pp. 1–29, 2018. doi: 10.3390/s18082674
A. Chlingaryan, S. Sukkarieh, and B. Whelan, “Machine learning approaches for crop yield prediction and nitrogen status estimation,” Sensors, vol. 18, no. 11, pp. 1–27, 2018. doi: 10.3390/s18113706
S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using deep learning for image-based plant disease detection,” Frontiers in Plant Science, vol. 7, pp. 1–10, 2016. doi: 10.3389/fpls.2016.01419
M. Smith, J. Mullins, and R. Smith, “Machine learning methods for irrigation decision support,” Agricultural Water Management, vol. 216, pp. 1–10, 2019. doi: 10.1016/j.agwat.2019.01.004
Y. Liu, Y. Wang, and C. Yang, “Short-term agricultural price forecasting using LSTM networks,” IEEE Access, vol. 8, pp. 1–12, 2020. doi: 10.1109/ACCESS.2020.2987654.
Morales A, Villalobos FJ. Using machine learning for crop yield prediction in the past or the future. Front Plant Sci. 2023 Mar 30;14:1128388. doi: 10.3389/fpls.2023.1128388. PMID: 37063228; PMCID: PMC10097960
D. J. Reddy and M. R. Kumar, “Crop Yield Prediction using Machine Learning Algorithm,” 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2021, pp. 1466-1470, doi: 10.1109/ICICCS51141.2021.9432236.
Mohanty SP, Hughes DP, Salathé M. Using Deep Learning for Image-Based Plant Disease Detection. Front Plant Sci. 2016 Sep 22;7:1419. doi: 10.3389/fpls.2016.01419. PMID: 27713752; PMCID: PMC5032846.
Fernando, H., Ha, T., Nketia, K.A. et al. Machine learning approach for satellite-based subfield canola yield prediction using floral phenology metrics and soil parameters. Precision Agric 25, 1386–1403 (2024). https://doi.org/10.1007/s11119-024-10116-1
Radwan, M., Alhussan, A.A., Ibrahim, A. et al. Potato Leaf Disease Classification Using Optimized Machine Learning Models and Feature Selection Techniques. Potato Res. 68, 897–921 (2025). https://doi.org/10.1007/s11540-024-09763-8
Yao, J., Tran, S.N., Sawyer, S. et al. Machine learning for leaf disease classification: data, techniques and applications. Artif Intell Rev 56 (Suppl 3), 3571–3616 (2023). https://doi.org/10.1007/s10462-023-10610-4