Artificial Intelligence and Machine Learning for Resource Optimization in Agriculture: A Review

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.