Solving agricultural challenges through mathematical optimization and simulation

Abstract

Agriculture today is a multidisciplinary field, increasingly influenced by data-driven strategies and computational tools. Mathematical modeling, statistical inference, optimization, time series analysis, geospatial methods, and artificial intelligence are some of the key mathematical domains that support research and management in agriculture. This comprehensive paper reviews and analyzes the broad applications of mathematics in agricultural research.Management, covering over 20 real-world examples, comparative evaluations of techniques, and future directions. It explores how mathematics aids in decision-making, yield prediction, resource allocation, disease modeling, and environmental impact assessment.