The present study deals with the identification of optimal crop plan to improve the net benefits from the farming activities for the study area under consideration.Three nature inspired metaheuristic techniques namely Differential Evolution (DE), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) are investigated to identify the most efficient crop plan to maximize the net farm benefits. Different resource constraints considered for the study are maximum available land area, ground water availability and cropped area for different crops. The obtained results are compared with the solutions from LINGO, software for Linear Programming (LP). The results reveal the net benefit per ha derived using DE, PSO, GA and LINGO are 73841.69, 73439.08, 70555.99 and 73841.65 Indian Rupees (INR) respectively for kharif crops and 20184.71, 20172.78, 19860.80 and 20184.70 `Indian Rupees (INR) respectively for rabi crops.

This paper presents a multi-objective optimization problem to identify an optimal crop plan by simultaneously maximizing profit and reducing groundwater utilization. An important economic and sustainable agricultural decision-making task is to make optimal use of resource constraints in a given planting period. To solve the problem, the NSGA II and NSGA III algorithms are used. The performance and effectiveness of both algorithms are examined by comparison of the results. In addition, the Pareto fronts are presented with graphical comparisons. The findings show that NSGA-III is a more viable tool than NSGA II to solve an optimal crop planning problem.