Authors: Mayuri Dongre, Harsh Upase, Krushnakant Shinde
Abstract: Artificial Intelligence (AI) has emerged as a transformative technology in modern agriculture, enabling sustainable and data-driven farming practices through precision agriculture techniques. This research paper presents a comprehensive study of AI-based technologies and their applications in sustainable precision agriculture. The study explores the integration of Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT), computer vision, robotics, drones, and sensor-based systems for improving agricultural productivity, resource optimization, and environmental sustainability. AI techniques are increasingly used for crop prediction, disease detection, soil analysis, irrigation management, yield forecasting, weed identification, and climate monitoring, helping farmers make accurate and timely decisions. The paper also highlights how precision agriculture minimizes the excessive use of water, fertilizers, and pesticides while enhancing crop quality and reducing environmental impact. Furthermore, the study examines recent advancements, real-world applications, challenges, and limitations. AI adoption in agriculture, including high implementation costs, lack of technical knowledge, data availability issues, and infrastructure constraints in rural areas. Precision agriculture harnesses data-driven techniques to optimize crop production, resource use, and sustainability. However, low-income countries like Bangladesh face a short- age of localized, high-quality datasets that reflect regional agroclimatic conditions and cropping practices.
