Crop yield prediction using data mining: an efficient data modeling approach

  • Abstract
  • Keywords
  • References
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  • Abstract

    Motivation for this research comes from the agricultural point of view that there is a lot of work for the farmers to be done manually. So this research deals with the automatic classification approach which provides help to the farmers and decrease their efforts. With the change of climate in the world, the mainstreams of the crops in the agricultural field are severely affected in performance in previous decades. The crop prediction yielding should be healthy to help the farmers for taking suitable procedures for selling and loading/storage. Such estimations on the yielding of the crops will also assist the industries based on the farming and production of the crops for development of the logistics of their commercials. Numerous approaches of estimating and crop yielding have been established in the previous times with variable success rates. This paper deals with the prediction of the wheat crop yielding using data mining approaches. The paper deals with the clustering approach using k-means clustering and feature extraction using independent component analysis and the classification is done using linear discriminant analysis and the simulation is done using MATLAB environment. From the result and discussions it can be noticed that our proposed approach is able to achieve less error rate probabilities and high signal to noise ratio. The proposed approach is able to achieve mean square error rate of 0.5 and peak signal to noise ratio of 33.18 db.



  • Keywords

    Agriculture; Crop Yielding; Data Mining; Learning Systems

  • References

      [1] Pudumalar, S., E. Ramanujam, R. Harine Rajashree, C. Kavya, T. Kiruthika, and J. Nisha. "Crop recommen-dation system for precision agriculture." In Advanced Computing (ICoAC), 2016 Eighth International Confe-rence on, pp. 32-36. IEEE, 2017.

      [2] S.S. Bhanose, K. A. Bogawar, A. G. Dhotre, and B. R. Gaidhani, "Crop and Yield Prediction Model", Inter-national journal 1, no. 1, 2016.

      [3] A. Shastry, H. A. Sanjay, and M. Hegde, "A parame-ter based ANFIS model for crop yield prediction", In Advance Computing Conference (IACC), 2015 IEEE International, pp. 253-257. IEEE, 2015.

      [4] S. Veenadhari, B. Misra, C. D. Singh. "Machine learning approach for forecasting crop yield based on climatic parameters." In Computer Communication and Informatics (ICCCI), 2014 International Conference on, pp. 1-5. IEEE, 2014.

      [5] A.A. Raorane, R. V. Kulkarni, "Data Mining: An effective tool for yield estimation in the agricultural sec-tor", International Journal of Emerging Trends of Tech-nology in Computer Science 1, no. 2, pp: 75-79, 2012.

      [6] 2016 13th International Joint Conference on, pp. 1-5. IEEE, 2016.

      [7] A. Malav, K. Kadam, P. Kamat, "Prediction Of Heart Disease Using K-Means and Artificial Neural Network as Hybrid Approach to Improve Accuracy", International Journal of Engineering and Technology 9, no. 4, 2017.

      [8] G.B. Huang, Qing-Yi GUO, Ren-Zhi Zhang, Lei PANG, Guangdi LI, Kwong Yin CHAN, and Ai-Zhong YU. "Effects of conservation tillage on soil moisture and crop yield in a phased rotation system with spring wheat and field pea in dryland." Acta Ecologica Sinica 4 (2006): 026.

      [9] M.E. Cerrato, A. M. Blackmer, "Comparison of models for describing; corn yield response to nitrogen fertilizer", Agronomy Journal 82, no. 1 (1990): 138-143.

      [10] W. Schlenker, M. J. Roberts, "Nonlinear tempera-ture effects indicate severe damages to US crop yields under climate change", Proceedings of the National Academy of sciences 106, no. 37, and pp: 15594-15598, 2009.

      [11] B.L. Ma, Lianne M. Dwyer, C. Costa, E. R. Cober, M. J. Morrison. "Early prediction of soybean yield from canopy reflectance measurements." Agronomy Journal 93, no. 6, pp: 1227-1234, 2001.

      [12] N.A. Hessling, “Relations between the Weather and the Yield of Wheat in the Argentine Republic", Monthly Weather Review 50, no. 6, and pp: 302-308, 1992.

      [13] P. Steduto, T. C. Hsiao, D. Raes, E. Fereres. "Aq-uaCrop—The FAO crop model to simulate yield re-sponse to water: I. Concepts and underlying principles." Agronomy Journal 101, no. 3, pp: 426-437, 2009.

      [14] A.M. Khorasani, M. R. S. Yazdi, and M. S. Safiza-deh, "Tool Life Prediction in Face Milling Machiningof 7075 Al by Using Artificial Neural Networks (ANN) and Taguchi Design of Experiment (DOE)", International Journal of Engineering and Technology 3, no. 1, 2011.

      [15] W. Fan, C. Chong, G. Xiaoling, Y. Hua, and W. Juyun, "Prediction of crop yield using big data", In Computational Intelligence and Design (ISCID), 2015 8th International Symposium on, vol. 1, pp. 255-260. IEEE, 2015.B.D.

      [16] N. Gandhi, L.J. Armstrong, O. Petkar, and A. K. Tri-pathy, "Rice crop yield prediction in India using support vector machines." In Computer Science and Software Engineering (JCSSE), 2016 13th International Joint Con-ference on, pp. 1-5. IEEE, 2016.

      [17] Hashmi, Adil, Divya Gupta, Yash Upadhyay, and Shruti Goel. "Swarm intelligence based approach for data clustering." International Journal of Innovative Research & Studies (IJiRS) (2013).

      [18] S. Pudumalar, E. Ramanujam, R. H. Rajashree, C. Kavya, T. Kiruthika, and J. Nisha, "Crop recommenda-tion system for precision agriculture." In Advanced Computing (ICoAC), 2016 Eighth International Confe-rence on, pp. 32-36. IEEE, 2017.

      [19] D.B. Lobell, C. B. Field, "Global scale climate–crop yield relationships and the impacts of recent warming." Environmental research letters two, no. 1, 2007.

      [20] L.F.E Marcelis, E. Heuvelink, J. Goudriaan. "Mod-elling biomass production and yield of horticultural crops: a review." Scientia Horticulturae 74, no. 1-2, pp: 83-111, 1998.

      [21] T. Palosuo, K. C. Kersebaum, C. Angulo, P. Hla-vinka, M. Moriondo, J. E. Olesen, R. H. Patil et al. "Si-mulation of winter wheat yield and its variability in dif-ferent climates of Europe: a comparison of eight crop growth models." European Journal of Agronomy 35, no. 3, pp: 103-114, 2011.

      [22] L. Serrano, I. Filella, and J. Penuelas. "Remote sens-ing of biomass and yield of winter wheat under different nitrogen supplies." Crop science 40, no. 3, pp: 723-731, 2000.

      [23] L. Serrano, I. Filella, and J. Penuelas. "Remote sens-ing of biomass and yield of winter wheat under different nitrogen supplies." Crop science 40, no. 3, pp: 723-731, 2000.

      [24] A. Chipanshi, Y. Zhang, L. Kouadio, N. Newlands, A. Davidson, H. Hill et al. "Evaluation of the Integrated Canadian Crop Yield Forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agri-cultural landscape." Agricultural and Forest Meteorology, pp: 137-150, 2015.

      [25] S. Asseng, F. Ewert, P. Martre, R. P. Rötter, D. B. Lobell, D. Cammarano, B. A. Kimball et al, "Rising temperatures reduce global wheat production." Nature Climate Change 5, no. 2, 2015.

      [26] B.D. Lobell, M. B. Burke, "On the use of statistical models to predict crop yield responses to climate change", Agricultural and Forest Meteorology 150, no. 11, pp: 1443-1452, 2010.




Article ID: 13157
DOI: 10.14419/ijet.v7i2.27.13157

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