Researchers reviewed how machine learning algorithms are being applied across agriculture, from predicting crop yields to detecting plant diseases and managing soil and water. The review maps the current state of AI tools in farming and highlights where gaps remain for future research.
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ML algorithms show strong performance across four core agricultural domains: crop yield prediction, disease identification, soil management, and water management.
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Multiple ML model types are in use, with no single algorithm dominating across all applications, indicating the field is still maturing.
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Significant research gaps remain, particularly in integrating multiple data types and translating models to real-world decision support systems at scale.
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