Advanced Machine Learning Architectures for Precision Crop Prediction: A Technical Evaluation of Model Performance, Challenges, and Future Directions

Abstract

Innovations driven by technology and data-driven methodologies, precision farming has emerged as a revolutionary area in modern agriculture. This study presents a review of recent advancements in Machine Learning (ML) techniques employed for Crop Prediction, followed by performance analysis of recent models (2019-2023). It explores the integration of advanced technologies, collaborative aims, and data-centric approaches aimed at overcoming the challenges in traditional agriculture. This paper presents the capabilities and complexity of precision farming through an analysis of various ML, Deep Learning, Reinforcement Learning, and Ensemble Learning models. Emphasising the important role of global collaboration and data-sharing initiatives provides information on the precision farming industry's changing environment and shows future developments in the field.