Multi-objective Crop Plan for Optimal Groundwater using Non-dominated Sorting Genetic Algorithms

Abstract

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.
275
Vafaeinejad, 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), 3068
3084.
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.
275
Vafaeinejad, 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), 3068
3084.
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.