Survey of Intelligent Optimization of Multicropping Strategies Across Irrigation Types Using Genetic Algorithms, Deep Learning, and Particle Swarm Optimization

Abstract

This research focuses on the intelligent optimization of multicropping strategies across various irrigation systems using advanced Artificial Intelligence (AI) techniques. In regions with diverse climatic and soil conditions, selecting the optimal crop combination is critical to maximize yield, resource efficiency, and sustainability. The study employs Genetic Algorithms (GA), Deep Learning (DL), and Particle Swarm Optimization (PSO) to identify and fine-tune crop combinations suited for clay-dominant soils under drip irrigation. The approach integrates seasonal variation, soil parameters, and crop characteristics to recommend high performance multicropping plans. The proposed methodology is expected to contribute to sustainable agricultural planning, increased profitability, and efficient resource use in semi-arid regions like Marulpatti in Tamil Nadu, India.