Effective crop planning is important for improving farmer’s profitability while ensuring sustainable use of agricultural resources. Selecting an optimal combination of crops becomes a complex decision-making problem. Regions with diverse cropping seasons and constrained inputs require more attention. This complexity arises from the need to simultaneously maximize economic returns and minimize resource consumption. In this work, a comparative analysis of several optimization algorithms is performed for addressing multi-objective crop planning problem. Unlike many studies that rely on benchmark problems, this evaluation uses real agricultural data from Telangana, India. Adopted model considers two conflicting goals: maximizing net economic return and minimizing fertilizer consumption. Practical constraints like total cultivable land and seasonal (Kharif and Rabi) allocation limits do exists. To ensure statistical reliability, each algorithm is executed over 30 independent runs. Performance is assessed not only by average objective values and convergence behavior but also by constraint satisfaction and the resulting crop allocation patterns. Results reveal that Multi-objective Dung Beetle Optimizer (MODBO) demonstrated a more balanced and practically viable profile, offering improved profit with controlled resource use, stable convergence, and a diversified crop pattern. This study reveals that for crop planning, the best algorithm is not merely the one that maximizes a single objective.

This study presented a comparative analysis of several metaheuristic algorithms for real-world crop planning problem. The crop planning problem used here, considers both economic and resource-related objectives, reflecting the multi-objective nature of crop planning. Analysis was conducted using classical and recently proposed metaheuristic algorithms under identical conditions. All algorithms were evaluated over 30 independent runs to ensure robustness and statistical reliability. Among the evaluated methods, the Multi-objective Dung Beetle Optimizer demonstrated a balanced performance. It achieved consistent improvement in net economic return while maintaining fertilizer usage close to the baseline level. In addition, its crop allocation patterns were more diversified and realistic. These characteristics make MODBO a practically suitable choice for regional crop planning applications under the considered formulation. The study highlights that algorithm selection should not be based solely on extreme objective values. Instead, stability, trade-off behavior, and decision-level outcomes should also be considered. This is particularly important for crop planning problems, where long-term sustainability and feasibility are critical. This study has certain limitations. Uncertainty related to climate variability, market price fluctuations, and policy changes was not explicitly modeled. Future work may extend the analysis by incorporating above uncertainties, additional sustainability indicators, or multi-season planning horizons. The application of hybrid or adaptive metaheuristic strategies may also be explored. Overall, the findings provide useful guidance for selecting appropriate metaheuristic algorithms for real-world crop planning problems.

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