Skip to main content

Maximizing Profits in Crop Planning Using Socio Evolution and Learning Optimization

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


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.

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-13-6569-0_8
  • Chapter length: 24 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   89.00
Price excludes VAT (USA)
  • ISBN: 978-981-13-6569-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   119.99
Price excludes VAT (USA)
Hardcover Book
USD   169.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16


  1. 1.

    ZAR is the South African currency.


  1. Dury J, Schaller N, Garcia F, Reynaud A, Bergez JE (2011) Models to support cropping plan and crop rotation decisions. A review. Agron Sustain Dev 32(2):567–580.

  2. Sarker RA, Talukdar S, Anwarul Haque AFM (1997) Determination of optimum crop mix for crop cultivation in Bangladesh. Appl Math Model 21(10):621–632. ISSN: 0307-904X.

  3. Sarker RA, Quaddus MA (2002) Modelling a nationwide crop planning problem using a multiple criteria decision making tool. Comput Ind Eng 42(24):541–553. ISSN: 0360-8352.

  4. Sarker R, Ray T (2009) An improved evolutionary algorithm for solving multi-objective crop planning models. Comput Electron Agric 68(2):191–199. ISSN: 0168-1699.

  5. Adeyemo J, Otieno F (2010) Differential evolution algorithm for solving multi-objective crop planning model. Agric Water Manag 97(6):848–856. ISSN: 0378-3774.

  6. Chetty S, Adewumi AO (2014) Comparison study of Swarm intelligence techniques for the annual crop planning problem. IEEE Trans Evol Comput 18(2):258–268.

  7. Ray T, Liew KM (2003) Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Trans Evol Comput 7(4):386–396.

  8. Xu Y, Cui Z, Zeng J (2010) Algorithm social emotional optimization, for nonlinear constrained optimization problems. In: Panigrahi BK, Das S, Suganthan PN, Dash SS (eds) Swarm, evolutionary, and memetic computing SEMCCO. Lecture notes in computer science, vol 6466. Springer, Berlin

    Google Scholar 

  9. Shastri AS, Kulkarni AJ (2018) Multi-cohort intelligence algorithm: an intra- and inter-group learning behaviour based socio-inspired optimisation methodology. Int J Parallel Emerg Distrib Syst 33(6):675–715

    Google Scholar 

  10. Liu Z-Z, Chu Z-Z, Song C, Xue X, Lu B-Y (2016) Social learning optimization (SLO) algorithm paradigm and its application in QoS-aware cloud service composition. Inform Sci 326:315–333. ISSN: 0020-0255.

  11. Satapathy S, Naik A (2016) Complex Intell Syst 2:173.

  12. Kumar M, Kulkarni AJ, Satapathy SC (2018) Socio evolution & learning optimization algorithm: a socio-inspired optimization methodology. Future Gener Comput Syst 81:252–272. ISSN: 0167-739X.

  13. Kulkarni AJ, Durugkar IP, Kumar M (2013) Cohort intelligence: a self supervised learning behavior. In: 2013 IEEE international conference on systems, man, and cybernetics, Manchester, 2013, pp 1396–1400.

  14. Season and Crop Report, Tamil Nadu (2012–2013), Department of Economics and Statistics, Chennai, season and crop report

    Google Scholar 

  15. Cropping Pattern: Cauvery Delta Zone, Department of Agrometerology, Tamil Nadu Agricultural University.

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to G. Jaya Brindha .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

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.

Download citation