Abstract
The rice is a major crop of India. It is the staple food of the eastern and southern parts of this country. The total yield of rice can be in a massive loss if it is affected by pests. The stem borer pest creates a lot of trouble. It affects the production of rice. As the control procedure with pesticide is not much effective on this pest, therefore, a forecasting model can play a major role in taking preventive measure. The objective of this research is to forecast the population occurrence of stem borer pest in the paddy. This paper highlights the improvement of the performance of backpropagation artificial neural network (BP-ANN) model using principal component analysis (PCA) to develop a prediction model by minimizing the error. The Convolution of data is proposed here instead of PCA to enhance the reduction of the dimensions of data which eventually results in less error in prediction.
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References
Yang, L.N., Peng, L., Zhang, L.M., Li-lian, Z., Yang, S.S., et al.: A prediction model for population occurrence of paddy stem borer (Scirpophaga incertulas), based on back propagation artificial neural network and principal components analysis. Comput. Electron. Agric. 68(2009), 200–206 (2009). https://doi.org/10.1016/j.compag.2009.06.003
Rad, M.R.N., Koohkan, S., Fanaei, H.R., Rad, M.R.P.: Application of Artificial Neural Networks to predict the final fruit weight and random forest to select important variables in native population of melon (Cucumis melo. Pahlavan). Sci. Hortic. 181, 108–112 (2015). https://doi.org/10.1016/j.scienta.2014.10.025
Sun, S., et al.: A comparison of models for the short-term prediction of rice stripe virus disease and its association with biological and meteorological factors. Acta Ecol. Sin. 36, 166–171 (2016). https://doi.org/10.1016/j.chnaes.2016.04.002
Günther, F., Fritsch, S.: neuralnet: Training of neural networks. R J. 2(1), 30–38 (2010)
Smith, B.A., Gerrit, H., McClendon, R.W.: Artificial neural networks for automated year-round temperature prediction. Comput. Electron. Agric. 68(2009), 52–61 (2009). https://doi.org/10.1016/j.compag.2009.04.003
Sengar, R.S., Kalpana, S.: Climate Change Effect on Crop Productivity. Taylor & Francis Group LLC, Milton Park (2015)
Seyedmohammadi, J., Esmaeelnejad, L., Ramezanpour, H.: Determination of a suitable model for prediction of soil cation exchange capacity. Model. Earth Syst. Environ. 2, 156 (2016). https://doi.org/10.1007/s40808-016-0217-4
Borovykh, A., Bohte, S., Oosterlee, C.W.: Conditional Time Series Forecasting with Convolutional Neural Networks. arXiv:1703.04691v3[stat.ML] 16 October 2017
Aaron, C., Cristopher, M., Newman, M.E.J.: Hierarchical structure and the prediction of missing links in networks. Nature 453, 98–101 (2008)
Cai, Z.X., Xu, G.Y.: Artificial Intelligence (AI) and Its Application. Tsinghua University Press, Beijing (1996)
Chen, X.J., Wang, C.N., Chen, S.T.: The Principle and Application of Neural Network, pp. 1–3. National Defense and Industry Press, Beijing (1995)
Maria, J.D.: Artificial neural networks as an alternative tool in pine bark volume estimation. Comput. Electron. Agric. 48(3), 235–244 (2005)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25(2012), 1097–1105 (2012)
Wang, Z., Yan, W., Oates, T.: Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline. CoRR, abs/1611.06455 (2016)
Hagan, M.T., Demuth, H.B., Beale, M.H., De Jesus, O.: Neural Network Design (2 edn, ebook). hagan.okstate.edu/nnd.html
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Paul, S., Banerjee, S., Biswas, U. (2019). An Approach Towards Development of a Stem Borer Population Prediction Model Using R Programming. In: Mandal, J., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2018. Communications in Computer and Information Science, vol 1031. Springer, Singapore. https://doi.org/10.1007/978-981-13-8581-0_12
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DOI: https://doi.org/10.1007/978-981-13-8581-0_12
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