Design and implementation of deep learning algorithms for an intelligent crop management system

This study designs a crop planting strategy based on deep learning, applied to optimize crop planting decisions in a specific rural area from 2024 to 2030. It aims to maximize profit by utilizing limited arable land resources under multiple uncertainties and constraints. This paper uses data preprocessing techniques and multi-objective optimization algorithms, with mixed-integer linear programming (MILP), QP-free, genetic algorithm (GA), and NSGA-II as core algorithms, to optimize planting strategies from both linear and nonlinear perspectives. In model design and validation, the performance of different algorithms is systematically compared, analyzing differences in runtime, resource usage, and accuracy. Experimental results show that NSGA-II better balances profit and resource efficiency under multi-objective conditions, while the QP-free model is suitable for cost-sensitive scenarios. The findings provide a highly efficient, intelligent decision support method for modern agriculture, with the potential to enhance crop production efficiency and achieve sustainable development.