Comprehensive Analysis of Crop Yield Prediction Using Deep Learning and Remote Sensing Techniques

This review explores the recent progress in crop yield prediction using deep learning and remote sensing. This highlights the effectiveness of CNNs and LSTMs in analyzing spa tiotemporal crop growth patterns. This study examines various approaches, including hybrid models and attention mechanisms, and notes their improved accuracy and interpretability. The key challenges include data quality, model generalizability, and interpretability. This study emphasizes the potential of these tech niques for addressing food security and sustainable agriculture. Future research directions include multisource data integration, transfer learning, and development of explainable AI models for agriculture.