Crop Yield Prediction

Crop yield prediction aims to forecast agricultural output using various data sources and advanced analytical techniques, primarily to enhance food security and inform agricultural decision-making. Current research heavily utilizes machine learning, particularly deep learning models like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and vision transformers, often incorporating multi-modal data including satellite imagery, weather patterns, soil properties, and even geospatial information. These models are being refined to improve accuracy, address spatial heterogeneity, and enhance explainability, leading to more reliable predictions at various scales from individual fields to entire countries. The resulting improvements in prediction accuracy have significant implications for optimizing resource allocation, mitigating risks associated with climate change, and ultimately improving agricultural efficiency and sustainability.