Machine-learning-based frameworks for reliable and sustainable crop forecasting

Abstract

Fueled by scientific innovations and data-driven approaches, accurate agriculture has arisen as a transformative sector in contemporary agriculture. The present investigation provides a summary of modern improvements in machine-learning (ML) strategies utilized for crop prediction, accompanied by a performance exploration of contemporary models. It examines the amalgamation of sophisticated technologies, cooperative objectives, and data-driven methodologies designed to address the obstacles in conventional agriculture. The study examines the possibilities and intricacies of precision agriculture by analyzing various models of deep learning, machine learning, ensemble learning, and reinforcement learning. Highlighting the significance of worldwide collaboration and data-sharing activities elucidates the evolving landscape of the precision farming industry and indicates prospective advancements in the sector.