Applications of machine learning and deep learning in agriculture: A comprehensive review

The digitalization of agriculture has increasingly integrated artificial intelligence (AI), machine learning (ML), and deep learning (DL) to address the challenges arising from population growth, climate change (CC), and resource limitations. This study provides a comprehensive review of the potential applications of AI techniques across various stages of agricultural production, with a particular focus on innovations that align with climate-smart agricultural practices. The review encompasses research conducted from 2018–2024, emphasizing the use of ML and DL in areas such as crop selection, land monitoring and management, water, soil and nutrient management, weed control, harvest and post-harvest practices,…

• KM
  King Mongkut's University of Technology Thonburi
• DO
  Department of Mathematics, University College London