Agricultural optimization refers to the systematic application of mathematical, computational, and analytical methods to improve the allocation and utilization of resources and the performance of agricultural production systems. It involves identifying optimal decisions concerning crop selection, planting patterns, irrigation scheduling, fertilizer application, pest management, harvesting operations, and agricultural machinery use under resource, environmental, and operational constraints ^[1][2]. Common approaches include mathematical programming, linear and nonlinear optimization, multi-objective optimization, evolutionary algorithms, simulation modeling, and data-driven decision support systems ^[2][3]. Agricultural optimization considers interactions among biological processes, environmental conditions, production inputs, and management practices to identify solutions that balance productivity, resource-use efficiency, and environmental performance. It can be applied at field, farm, and regional scales to support production planning, resource management, and the sustainable development of agricultural systems ^[1][3].