Researchers reviewed how machine learning is being applied across agriculture, from predicting crop yields to detecting plant diseases and managing soil and water. The review maps out which AI methods work best for which farming problems and highlights where the field still has gaps.
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Machine learning algorithms have been applied across four major agricultural domains: crop yield prediction, disease identification, soil and water management, and decision support systems.
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The review identifies persistent research gaps alongside current state-of-the-art methods, suggesting the field is still maturing rather than solved.
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Multiple ML model types are compared, indicating no single algorithm dominates across all agricultural applications.
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