Yield prediction, pest and disease diagnosis, soil fertility mapping, precision irrigation scheduling, and food quality assessment using machine learning and deep learning algorithms

Ajith, S., S. Vijayakumar, and N. Elakkiya. "Yield prediction, pest and disease diagnosis, soil fertility mapping, precision irrigation scheduling, and food quality assessment using machine learning and deep learning algorithms." Discover Food 5, no. 67 (2025): 1-23.

The growing demand for food grains amidst resource constraints necessitates advancements in crop management. Artificial intelligence, particularly machine learning and deep learning, is revolutionizing agricultural practices by enabling data-driven, precise, and sustainable solutions. This review synthesizes advancements in artificial intelligence applications across key domains, including crop yield prediction, precision irrigation, soil fertility mapping, insect pest and disease forecasting, and foodgrain quality assessment. Artificial intelligence algorithms efficiently process vast datasets from unmanned aerial vehicles, ground vehicles, and satellites, enabling precise and timely interventions. Artificial intelligence-driven tools automate pest detection and classification, optimize irrigation with minimal human input, generate high-resolution soil fertility maps, and enhance foodgrain quality assessment through rapid defect and contaminant detection. Artificial intelligence-powered precision irrigation integrates real-time soil moisture data and weather predictions for optimized water usage. Similarly, artificial intelligence-driven soil fertility mapping not only enables high-resolution assessments but also facilitates real-time monitoring of nutrient dynamics, supporting sustainable land management. In pest and disease detection, artificial intelligence systems combining image processing and real-time analytics demonstrate promise for early intervention. Artificial intelligence integration into foodgrain quality assessment leverages hyperspectral imaging and predictive models to enhance grading, adulteration detection, and contaminant screening, contributing to food safety and market competitiveness. Furthermore, advancements in transfer learning and data augmentation have improved artificial intelligence adoption in regions with limited datasets. While artificial intelligence technologies promise to boost agricultural productivity and sustainability, their efficacy and scalability hinges on data quality, diversity, and availability.