Advertisement

Genetic Algorithm to Find Most Optimum Growing Technique for Multiple Cropping Using Big Data

  • Vinamra DasEmail author
  • Sunny Jain
Conference paper
  • 165 Downloads
Part of the Lecture Notes on Multidisciplinary Industrial Engineering book series (LNMUINEN)

Abstract

In the present scenario, it is extremely important for any farmer to increase his farm productivity and using multi-farming techniques; it is one of the most suitable ways to achieve that (Paudel in J Nepal Agricu Res Council 2:37–45, 2016 [1]). Many new farming techniques are being introduced to which a general farmer has no access to and hence his growth rate is monotonous. Even after having the cutting-edge technology and farming techniques, a general farmer has no access to any of it, i.e. the outreach of information technology in farming is still very low. As the number of parameters to optimize farm productivity increases, so thus the permutations of number of techniques and hence expertise is needed to analyse the best farming technique for the given scenario. Given so many existing techniques which vary over even a slight change in parameters, only the experts in farming are adaptable to them and hence it is extremely important to automate the technique generation process so as to put the capability of generating the best farming output to a non-farming expert. From the farmer’s point of view, smart farming should provide him with the best crop output in the most sustainable manner. Moreover, multiple cropping over single piece of land has become a necessity to meet the financial requirements and the demand-supply chain in the market. Due to multiple crops being planted on the same piece of land, it makes the technique generation process more ambiguous and time-consuming. To tackle this problem, a big data analysis of farming parameters might prove to be helpful (Wolfert et al. in Agricu Syst 153:69–80, 2014 [2]). Big data analysis helps in exploiting large datasets computationally to observe hidden patterns, trends and outcome of each technique. The combination of using smart farming with the big data for multiple cropping can provide the most well-analysed results and the complex patterns which are not perceivable to humans in providing the most optimum use of farming resources under the given constraints. Using an analysis algorithm over the big-dataset might be able to provide faster and precise techniques over the complex set of quantifiable parameters and widely changing constraints.

Keywords

Smart farming Multiple cropping Big data 

References

  1. 1.
    Paudel, M.: Multiple cropping for raising productivity and farm income of small farmers. J. Nepal Agricu. Res. Council 2, 37–45 (2016).  https://doi.org/10.3126/jnarc.v2i0.16120CrossRefGoogle Scholar
  2. 2.
    Wolfert, S., Ge, L., Verdouw, C., Bogaardt, M.J.: Big data in smart farming–a review. Agric. Syst. 153, 69–80 (2017)CrossRefGoogle Scholar
  3. 3.
    Dwivedy, N.: Challenges faced by the agriculture sector in developing countries with special reference to India. Int. J. Rural Stud. 18(2) (2011)Google Scholar
  4. 4.
    More than 55% of Indians make a living from farming. Here’s how we can double their income. (2018). Retrieved from https://www.weforum.org/agenda/2017/10/more-than-55-of-indians-make-a-living-from-farming-heres-how-we-can-double-their-income/
  5. 5.
    Gumaste, S.S., Kadam, A.J.: Future weather prediction using genetic algorithm and FFT for smart farming. In: Computing Communication Control and automation (ICCUBEA), 2016 International Conference on IEEE pp. 1–6 (2016).  https://doi.org/10.1109/iccubea.2016.7860028
  6. 6.
    Prathibha, S.R., Hongal, A., Jyothi, M.P.: IOT based monitoring system in smart agriculture. In: Recent Advances in Electronics and Communication Technology (ICRAECT), 2017 International Conference on IEEE, pp. 81–84 (2017).  https://doi.org/10.1109/icraect.2017.52
  7. 7.
    Ruoff, E.: Optimizing Crop Land Allocation for Smallholder Farmers in Central Uganda (2017)Google Scholar
  8. 8.
    Olakulehin, O.J., Omidiora, E.O.: A genetic algorithm approach to maximize crop yields and sustain soil fertility. Net J. Agricu. Sci. 2(3), 94–103 (2014)Google Scholar
  9. 9.
    Palmer, A.H.: The agricultural significance of sunshine as illustrated in California. Mon. Weather Rev. 48(3), 151–154 (1920).  https://doi.org/10.1175/1520-0493(1920)48%3c151:tasosa%3e2.0.co;2CrossRefGoogle Scholar
  10. 10.
    Soni, N., Kumar, T.: Study of various mutation operators in genetic algorithms. Int. J. Comput. Sci. Inf. Technol. 5(3), 4519–4521 (2014)MathSciNetGoogle Scholar
  11. 11.
    Aktar, W., Sengupta, D., Chowdhury, A.: Impact of pesticides use in agriculture: their benefits and hazards. Interdisc. Toxicol. 2(1), 1–12 (2009)CrossRefGoogle Scholar
  12. 12.
    Hermawanto, D.: Genetic algorithm for solving simple mathematical equality problem (2013). arXiv preprint arXiv:1308.4675
  13. 13.
    Carr, J.: An introduction to genetic algorithms. Senior Project 1(40), 7 (2014)Google Scholar
  14. 14.
    Walpole, R.E.: Probability & Statistics for Engineers & Scientists, 9th edn, Prentice Hall (2012)Google Scholar
  15. 15.
    Moula, M.S., Hossain, M.S., Farazi, M.M., Ali, M.H., Mamun, M.A.A.: Effects of consecutive two years tobacco cultivation on soil fertility status at Bheramara Upazilla in Kushtia District. J. Rice Res 6(1), 190 (2018).  https://doi.org/10.4172/2375-4338.1000190
  16. 16.
    Jayaraman, P.P., Yavari, A., Georgakopoulos, D., Morshed, A., Zaslavsky, A.: Internet of things platform for smart farming: experiences and lessons learnt. Sensors 16(11), 1884 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.VIT VelloreVelloreIndia

Personalised recommendations