ARTIFICIAL INTELLIGENCE IN PRECISION AGRICULTURE: ADVANCING CROP PRODUCTIVITY AND SUSTAINABLE FARMING SYSTEMS

Abstract

In this review, the increasingly important involvement of Artificial Intelligence (AI) in changing traditional agriculture to intelligent precision agriculture is critically analyzed. The role and applications of different AI technologies such as machine learning, deep learning, computer vision, robotics, Internet of Things (IoT), remote sensing, and UAVs in monitoring crops, early identification of pests and diseases, precision irrigation, nutrient management, yield prediction, and automation of farms are elaborated. In addition, ways in which AI could help achieve sustainability in agriculture through efficient use of resources, reduced environmental impact, improved climate resilience, and enhanced agricultural productivity despite food demands, water shortages, climate change, and decreasing arable land are considered. Furthermore, different obstacles that hinder AI adoption are critically assessed. Such barriers include data heterogeneity, poor infrastructure, expensive implementations, lack of transparency algorithms, and digital divide, especially in smallholder agricultural systems. Lastly, emerging AI technologies like generative AI, digital twins, explainable AI, edge computing, and autonomous agriculture are discussed.