Artificial Intelligence and Machine Learning for Resource Optimization in Agriculture: A Review
Shyam Kumar Nunna *
Department of Agronomy, Agricultural College, Naira, India.
Srinivasa Rao Marada
Agricultural Research Station, Ragolu, Srikakulam, India.
Sarath Kumar Duvvada
Department of Agronomy, Agricultural College, Naira, India.
Govindha Rao Seepana
Department of Statistics and Computer Applications, Agricultural College, Naira, India.
Rajendra Kumar Bendi
Department of Agronomy, Agricultural College, Naira, India.
Hemalatha Kutikuppala
Department of Agronomy, Agricultural College, Naira, India.
Upendra Rao Annepu
Department of Agronomy, Agricultural College, Naira, India.
*Author to whom correspondence should be addressed.
Abstract
Efficient management of agricultural resources is increasingly important due to growing food demand, climate variability and limited land and water availability. Artificial intelligence (AI) and machine learning (ML) are emerging as powerful tools to enhance resource optimization in modern agriculture. These technologies support data-driven decision making by integrating data from soil sensors, weather forecasts, satellite imagery and Internet of Things (IoT) devices. This enables precise crop monitoring and timely farm management practices. AI and ML applications such as crop yield prediction, pest and disease detection, smart irrigation, weed control and nutrient management help reduce input wastage while maintaining or improving productivity. They allow real-time, site-specific interventions, helping farmers respond effectively to climate uncertainties and resource limitations. However, challenges like high implementation costs, lack of technical knowledge, data security issues and poor infrastructure especially in developing regions limit adoption. To overcome these barriers, farmer training, supportive government policies, and transparent data governance are essential. Overall, AI and ML offer a promising pathway toward sustainable agriculture, improved profitability, and long-term food security.
Keywords: Digital technology, data management, precision agriculture, decision-making, internet of things, sustainable agriculture.