AI-Powered Smart Irrigation: A Case Study on Applying Genetic Algorithms and Particle Swarm Optimization for Water Conservation in Agricultural Projects

Abstract

Agriculture worldwide, particularly in arid and semi-arid regions, is severely affected by water scarcity, which has become a major global challenge, this paper describes development of the proposed artificial intelligence-based system, Smart Drop that optimizes irrigation water to address this issue. The system was tested and deployed through a 4.25-kilometer (internal diameter: 800 millimeters) pipe network irrigating 147 hectares of farmland at the design flow rate of 0.25 cubic meters per second as described in Case Study. The combination of machine learning models with genetic algorithms and particle swarm optimization was applied for prediction of irrigation needs and optimized irrigation water supply. Three models were established through the use of machine learning: random forest (R²=0.9965; RMSE= 0.0491 millimeters per day), Gradient Boosting (R² = 0.9976, and RMSE = 0.0487 millimeter/day) as well as neural networks models (R2 = 0.9943, and MSE = 0.0514). Compared with the traditional irrigation methods, this system reduced water use by 23.42% in irrigation (797,731) cubic meters over a period of 1,000 days), maintaining optimal crop conditions, the savings to total cost was estimated at US$121,036.62 (23.4%), indicating that the combination of reduced water use along with reduced energy consumption produced an economically viable alternative. The hydraulic performance results show acceptable pressure distribution through pipeline and overall losses within system are 1.0 meter drops across each section 9.78 kilopascals. This research is a data-driven precision irrigation solution for sustainable application in different agricultural environments.