AI in Agriculture: Techniques and Applications

Abstract

The role of agriculture in providing food security globally cannot be overstated, but it is associated with various complex issues, and agricultural researchers have found that Machine Learning (ML), as a subset of Artificial Intelligence (AI), is helpful in precision decision-making through learning from multiple agricultural data types. This paper reviews various state-of-the-art ML algorithms applied to crop yield, disease identification, soil/water management, and decision support systems. The paper summarizes various ML models, applications, and trends, as well as research challenges and gaps, aiming to offer insights to guide future research and applications.