Multi-objective Crop Plan for Optimal Groundwater using Non-dominated Sorting Genetic Algorithms
Agriculture plays an exceedingly pivotal role in the future growth and development of a nation, relying heavily on its water and land resources. To achieve maximum agricultural productivity, it is crucial to ensure timely and adequate supply of both land and water for irrigated farming. This necessitates precise planning and execution of water resource management, coupled with the incorporation of modern technologies to optimize the utilization of available resources. A significant task in economic and sustainable agricultural decision-making involves optimizing resource constraints within a given planting period. Considering the socioeconomic conditions, the present study tackles a multi-objective optimization problem to identify an optimal crop plan that simultaneously maximizes profit while minimizing groundwater usage. In single-objective optimization problems, determining the superior solution was straightforward by comparing objective function values, whereas in multi-objective optimization, dominance criteria are employed for determination. The study focuses on the genetic algorithm based NSGA-II and NSGA-III algorithms, comparing their performance and effectiveness through result analysis. Additionally, graphical comparisons are presented to showcase the Pareto fronts. The findings indicate that NSGA-III is a more viable tool for addressing optimal crop planning problems compared to NSGA-II. problem. ARPN Journal of Engineering and Applied Sciences, 11, 4079-4086.Kuo, S.F., Merkley, G.P., and Liu, C.W. (2000). Decision support for irrigation project planning using a genetic algorithm. Agricultural Water Management, 45(3), 243-266.275Vafaeinejad, A. (2016). Cropping Pattern Optimization by Using of TOPSIS and Genetic Algorithm Based on the Capabilities of GIS. Iranian journal of Ecohydrology, 3(1), 69-82.Van Veldhuizen, D.A., and Lamont, G.B. (2000). On measuring multiobjective evolutionary Lalehzari, R., Boroomand Nasab, S., Moazed, H., and Haghighi, A. (2016). Multiobjective management of water allocation to algorithm performance. In Proceedings of the 2000 Congress on Evolutionary Computation. CEC00, 1, 204-211, IEEE.sustainable irrigation planning and optimal cropping pattern. Journal of Irrigation and Drainage Engineering, 142(1), 05015008.Mansourifar, M., Almassi, M., Borghaee, A.M., and Moghadassi, R. (2013). Optimization crops pattern in variable field ownership. World Applied Sciences Journal, 21(4), 492-497.Marko, O., Pavlović, D., Crnojević, V., and Deb, K. (2019). Optimisation of crop configuration using NSGA-III with categorical genetic operators. In Proceedings of the Genetic and Evolutionary Computation Conference Companion.Márquez, A.L., Baños, R., Gil, C., Montoya, M.G., Manzano‐Agugliaro, F., and Montoya, F.G. (2011). Multi‐objective crop planning using pareto‐based evolutionary algorithms. Agricultural Economics, 42(6), 649-656.Mwiya, R.M., Zhang, Z., Zheng, C., and Wang, C. (2020). Comparison of Approaches for Irrigation Scheduling Using AquaCrop and NSGA-III Models under Climate Uncertainty. Sustainability, 12(18), 7694.Nath, K., Jain, R., Marwaha, S., Roy, H.S. and Arora, A. (2020). Identification of optimal crop plan using nature inspired metaheuristic algorithms. Indian Journal of Agricultural Sciences, 90(8), 1587-92.Olakulehin, O.J., and Omidiora, E.O. (2014). A genetic algorithm approach to maximize crop yields and sustain soil fertility. Net Journal of Agricultural Science, 2(3), 94-103.Oluwole, A.A., Oludayo, O.O., and Josiah, A. (2014). A comparative study of state-of-the-art evolutionary multi-objective algorithms for optimal crop-mix planning. International Journal of Agricultural Science and Technology, 2(1), 1-9.Pal, B.B., Chakraborti, D., and Biswas, P. (2009). A genetic algorithm based hybrid goal programming approach to land allocation problem for optimal cropping plan in agricultural system. In International Conference on Industrial and Information Systems (ICIIS).Raju, K.S., Vasan, A., Gupta, P., Ganesan, K., and Mathur, H. (2012). Multi-objective differential evolution application to irrigation planning. ISH Journal of Hydraulic engineering, 18(1), 54-64.Sadati, S.K., Speelman, S., Sabouhi, M., Gitizadeh, M., and Ghahraman, B. (2014). Optimal irrigation water allocation using a genetic algorithm under various weather conditions. Water, 6(10), 30683084.Srinivas, N., and Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary computation, 2(3), 221-248.Sarker, R., and Ray, T. (2009). An improved evolutionary algorithm for solving multi-objective crop planning models. Computers and electronics in agriculture, 68(2), 191-199.Sarma, A.K., Misra, R., and Chandramouli, V. (2006). Application of genetic algorithm to Opsearch, 43(3), 320-329.determine optimal cropping pattern. problem. ARPN Journal of Engineering and Applied Sciences, 11, 4079-4086.Kuo, S.F., Merkley, G.P., and Liu, C.W. (2000). Decision support for irrigation project planning using a genetic algorithm. Agricultural Water Management, 45(3), 243-266.275Vafaeinejad, A. (2016). Cropping Pattern Optimization by Using of TOPSIS and Genetic Algorithm Based on the Capabilities of GIS. Iranian journal of Ecohydrology, 3(1), 69-82.Van Veldhuizen, D.A., and Lamont, G.B. (2000). On measuring multiobjective evolutionary Lalehzari, R., Boroomand Nasab, S., Moazed, H., and Haghighi, A. (2016). Multiobjective management of water allocation to algorithm performance. In Proceedings of the 2000 Congress on Evolutionary Computation. CEC00, 1, 204-211, IEEE.sustainable irrigation planning and optimal cropping pattern. Journal of Irrigation and Drainage Engineering, 142(1), 05015008.Mansourifar, M., Almassi, M., Borghaee, A.M., and Moghadassi, R. (2013). Optimization crops pattern in variable field ownership. World Applied Sciences Journal, 21(4), 492-497.Marko, O., Pavlović, D., Crnojević, V., and Deb, K. (2019). Optimisation of crop configuration using NSGA-III with categorical genetic operators. In Proceedings of the Genetic and Evolutionary Computation Conference Companion.Márquez, A.L., Baños, R., Gil, C., Montoya, M.G., Manzano‐Agugliaro, F., and Montoya, F.G. (2011). Multi‐objective crop planning using pareto‐based evolutionary algorithms. Agricultural Economics, 42(6), 649-656.Mwiya, R.M., Zhang, Z., Zheng, C., and Wang, C. (2020). Comparison of Approaches for Irrigation Scheduling Using AquaCrop and NSGA-III Models under Climate Uncertainty. Sustainability, 12(18), 7694.Nath, K., Jain, R., Marwaha, S., Roy, H.S. and Arora, A. (2020). Identification of optimal crop plan using nature inspired metaheuristic algorithms. Indian Journal of Agricultural Sciences, 90(8), 1587-92.Olakulehin, O.J., and Omidiora, E.O. (2014). A genetic algorithm approach to maximize crop yields and sustain soil fertility. Net Journal of Agricultural Science, 2(3), 94-103.Oluwole, A.A., Oludayo, O.O., and Josiah, A. (2014). A comparative study of state-of-the-art evolutionary multi-objective algorithms for optimal crop-mix planning. International Journal of Agricultural Science and Technology, 2(1), 1-9.Pal, B.B., Chakraborti, D., and Biswas, P. (2009). A genetic algorithm based hybrid goal programming approach to land allocation problem for optimal cropping plan in agricultural system. In International Conference on Industrial and Information Systems (ICIIS).Raju, K.S., Vasan, A., Gupta, P., Ganesan, K., and Mathur, H. (2012). Multi-objective differential evolution application to irrigation planning. ISH Journal of Hydraulic engineering, 18(1), 54-64.Sadati, S.K., Speelman, S., Sabouhi, M., Gitizadeh, M., and Ghahraman, B. (2014). Optimal irrigation water allocation using a genetic algorithm under various weather conditions. Water, 6(10), 30683084.Srinivas, N., and Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary computation, 2(3), 221-248.Sarker, R., and Ray, T. (2009). An improved evolutionary algorithm for solving multi-objective crop planning models. Computers and electronics in agriculture, 68(2), 191-199.Sarma, A.K., Misra, R., and Chandramouli, V. (2006). Application of genetic algorithm to Opsearch, 43(3), 320-329.determine optimal cropping pattern.