Design and Experimental Study of an Intelligent Agricultural Management System Based on Artificial Intelligence Technology
Abstract：To improve agricultural production efficiency, optimize resource utilization, and achieve sustainable development, this study analyzed three aspects: crop prediction and yield optimization, pest and disease detection and prevention, and irrigation and fertilization optimization, using machine learning, deep learning, Internet of Things (IoT), and big data analysis technologies. The XGBoost regression model was used to predict crop growth and yield, the YOLO algorithm was applied for real-time pest and disease detection, and a reinforcement learning algorithm was used to optimize irrigation and fertilization strategies. The research results show that the system can increase crop yield by 15% and reduce resource consumption by 20%. The proposed intelligent agricultural management system has high application potential and can significantly improve the efficiency and sustainability of agricultural production.
Keywords：Artificial IntelligenceAgricultural ManagementYield OptimizationPest and Disease DetectionIrrigation Optimization
Publication Date：2025-04-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：3( 41-43 )