Smart Mathematical Model for Agricultural Crop Planning and Resource Optimization

Abstract

Agricultural crop planning is a complex decision-making process that must balance land, water, labor, and capital constraints against the goal of maximizing farm profitability and sustainability. This paper presents a smart mathematical optimization model that integrates linear and non-linear programming principles with heuristic search to generate optimal crop-area allocation plans under multiple resource constraints. The proposed model formulates an objective function for net profit maximization subject to land, water, labor, and fertilizer budget constraints, and is solved using a hybrid Simplex-metaheuristic approach. A structured flowchart describes the end-to-end methodology, from data acquisition to validated output generation. Simulation results across five representative crops (wheat, rice, maize, soybean, and cotton) show an average profit improvement of 16.6% and water-use reduction of approximately 15% compared to traditional allocation practices. A tabulated review of relevant literature from 2020 to 2022 contextualizes the proposed approach against existing optimization-based agricultural planning methods. The findings demonstrate that mathematically-guided crop planning can meaningfully improve resource-use efficiency and farm income while remaining adaptable to climatic and market variability.