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Abstract

This research synthesizes recent advancements in crop yield prediction, focusing on the application of sophisticated computational models. Studies explore deep learning architectures, comparative analyses of machine learning algorithms including ensemble methods, and the integration of remote sensing data with crop simulation models. Artificial Neural Networks and hybrid modeling approaches are examined for their effectiveness in handling complex environmental and crop data interactions. The role of scalable data platforms like Google Earth Engine and techniques such as transfer learning and data assimilation in enhancing prediction accuracy and accessibility is discussed. Probabilistic models offering uncertainty quantification are also presented as crucial for risk assessment in agriculture.

Keywords: Crop Yield Prediction脥戮 Machine Learning脥戮 Deep Learning脥戮 Remote Sensing脥戮 Ensemble Learning脥戮 Artificial Neural Networks脥戮 Crop Simulation Models脥戮 Data Assimilation脥戮 Transfer Learning脥戮 Gaussian Process Regression