Author(s): Singh, Khushwant ; Yadav, Mohit ; Barak, Dheerdhwaj ; Bansal, Shivani ; Moreira, Fernando
Date: 2025
Origin: Repositório da Universidade Portucalense
Subject(s): Crop prediction; machine learning; deep learning; smart farming; precision agriculture; Ciências Naturais - Ciências da Computação e da Informação
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.
