A Systematic Analysis of Current Developments and Potential Challenges in Applied Deep Learning-Based Seed Yield Prediction

Seed yield prediction is crucial in modern agriculture, aiding farmers and stakeholders in making informed decisions regarding crop management, resource allocation, and harvest planning. Traditionally, seed yield prediction relied on empirical models and historical data, which often lacked accuracy and robustness, particularly in dynamic agricultural environments. However, with the advent of deep learning (DL) techniques, there has been a paradigm shift in seed yield prediction research, enabling the development of sophisticated models capable of analyzing complex spatial and temporal data with unprecedented accuracy.