Multimodal Machine Learning for Crop Yield Prediction in Precision Agriculture

Abstract

Increased focus on precise agricultural insurance, climate-risk management, food-security planning, and precision agriculture has led to a greater interest in accurate prediction of crop yield. The development of Earth observation, meteorological reanalysis, soil mapping, field sensing, and Machine Learning (ML) has moved yield forecasting beyond single-source statistical models to multimodal predictive models. This critical analysis covers the emerging trends in ML and Deep Learning (DL) to predict crop yields, with a particular focus on the combination of remote-sensing images, weather patterns, soil and topographical data, crop-management data, and crop-growth indicators. Random forest, gradient boosting, convolutional neural networks, recurrent networks, and hybrid process-ML are effective predictors in representative studies; however, reported performance is highly dependent on the type of crop, the scale of the spatial area, the quality of the data, representation of the features, and the approach to validation. The multimodal integration can be specifically beneficial due to the description of complementary aspects of the crop-production system by different data sources, but the mismatch in spatial resolution, uncertainty in ground truth, leakage of information, poor geographic transferability, and poor model interpretability are the critical issues. New directions are process-guided learning, explainable AI, transfer learning, foundation models, uncertainty-aware forecasting, and computationally efficient operational systems. Future developments should hence be assessed in both predictive accuracy and robustness, reproducibility, agronomic plausibility, and practical decision value.