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Maximizing Profits in Crop Planning Using Socio Evolution and Learning Optimization

Part of the Studies in Computational Intelligence book series (SCI,volume 828)

Abstract

Crop planning is the strategy of choosing the appropriate crop and allocating the field area for cultivation. The objective of crop planning is to choose the suitable crop among various competing crops that can be grown during a particular season. A best cropping plan must satisfy the objectives like maximizing the profitability and productivity of the crop and allocating the scarce resources such as water for irrigation and field area. Crop planning must be optimized, so that it maximizes the returns from every part of the land used for cultivation which satisfies all the constraints. Social evolution and learning optimization (SELO)—a sociocultural inspired metaheuristic algorithm based on human social behavior is explored to solve this crop planning problem. SELO is initially tested on four benchmark functions out of which two are unconstrained and two are constrained. SELO is further explored to solve a benchmark optimization problem on crop planning in Vaalharts Irrigation Scheme (VIS) in South Africa. SELO increases the total profits by ZAR 4,689,569 (ZAR is the South African currency.) when compared with the current practices. To evaluate the performance of SELO, the results are compared with the existing swarm intelligence metaheuristic techniques such as Cuckoo search, Firefly algorithm, Glowworm swarm optimization, and Genetic algorithm. While comparing with Cuckoo search (most effective swarm technique in VIS crop planning), SELO increases the total profits by ZAR 255,489 (ZAR is the South African currency.). In addition, a case study on decision support systems for crop planning in Cauvery Delta Region are attempted. SELO is investigated for the real-time statistics obtained for Thanjavur, Thiruvarur, Nagapattinam, and Tiruchirapalli districts of Cauvery Delta Region in Tamilnadu, India. It is observed that SELO optimizes the land use among various available crops and increases the profits by 8.92, 1.39,  2.98, and  9.16 crores ( is the Indian currency.) when compared with the corresponding current practices in Thanjavur, Thiruvarur, Nagapattinam, and Tiruchirapalli districts, respectively. The obtained results reveal that SELO has a good potential to solve these crop planning problems.

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Notes

  1. 1.

    ZAR is the South African currency.

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Correspondence to G. Jaya Brindha .

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Jaya Brindha, G., Gopi, E.S. (2019). Maximizing Profits in Crop Planning Using Socio Evolution and Learning Optimization. In: Kulkarni, A.J., Singh, P.K., Satapathy, S.C., Husseinzadeh Kashan, A., Tai, K. (eds) Socio-cultural Inspired Metaheuristics. Studies in Computational Intelligence, vol 828. Springer, Singapore. https://doi.org/10.1007/978-981-13-6569-0_8

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