(untitled) (Journal of Asian Development Studies, 2025)
The agricultural sector is transforming with the integration of Artificial Intelligence (AI), enhancing productivity, sustainability, and decision-making. AI technologies, including machine learning (ML), deep learning (DL), computer vision, robotics, and the Internet of Things (IoT) are being applied to optimize various farming operations. This study is based on a descriptive literature review published in national and international journals and explores key AI trends such as precision farming, crop health monitoring, pest and weed detection, yield prediction, smart irrigation, agricultural robotics, climate forecasting, and supply chain optimization. AI-powered autonomous machinery reduces labor dependency while drone- assisted disease detection and AI-driven pest management minimize resource waste and environmental impact. AI-enabled predictive models have been shown to improve crop yield by 26%, reduce water use by 41%, and cut chemical usage by 33% (Addas et al., 2023). Despite its numerous benefits, AI adoption faces challenges such as high implementation costs, data accessibility, and the need for technical expertise. Ethical concerns, data privacy issues, and the potential reinforcement of power hierarchies in agriculture also pose significant challenges.