Convolution and Recurrent Hybrid Neural Network for Hevea Yield Prediction
Keywords:convolutional neural networks, feature selection, recurrent neural networks, rubber yield prediction, weather prediction
Deep learning techniques have been used effectively for rubber crop yield prediction. A hybrid of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) is the best technique for crop yield prediction because it can effectively handle uncertainty of features. Hence, in this paper, a hybrid CNN-RNN method is proposed to forecast Hevea yields based on environmental data in Kerala state, India. The proposed hybrid CNN-RNN method reduces the internal covariate shift of CNN by batch normalization and solves the gradient vanishing or exploding problem of RNN using LSTM with a cell activation mechanism. The proposed method has three essential characteristics: (i) it captures the time dependency of environmental factors and improves the inherent computational time; (ii) it is capable of generalizing the yield prediction under uncertain conditions without loss of prediction accuracy; (iii) combined with the back propagation and feed forward method it can reveal the extent to which samples of weather conditions and soil data conditions are suitable to provide a clear boundary between rubber yield variations.
Rivano, F., Mattos, C.R., Cardoso, S.E., Martinez, M., Cevallos, V., Le Guen V. & Garcia, D., Breeding Hevea Brasiliensis for Yield, Growth and SALB Resistance for High Disease Environments, Industrial Crops and Products, 44, pp. 659-670, 2013.
Golbon, R., Ogutu, J.O., Cotter, M. & Sauerborn, J., Rubber Yield Prediction by Meteorological Conditions Using Mixed Models and Multi-model Inference Techniques, International Journal of Biometeorology, 59(12), pp. 1747-1759, 2015.
Jeong, J.H., Resop, J.P., Mueller, N.D., Fleisher, D.H., Yun, K. & Butler, E.E., Random Forests for Global and Regional Crop Yield Predictions, PloS One, 11(6), 2016.
Romero, J.R., Roncallo, P. F., Akkiraju, P.C., Ponzoni, I., Echenique, V. C. & Carballido, J.A., Using Classification Algorithms for Predicting Durum Wheat Yield in the Province of Buenos Aires, Comput. Electron. in Agric. 96, pp. 173-179, 2013.
LeCun, Y., Bengio, Y. & Hinton, G., Deep Learning, Nature, 521(7553), pp. 436-444, 2015.
Khaki, S. & Wang, L., Crop Yield Prediction Using Deep Neural Networks, Frontiers in Plant Science, 10, 621, 2019.
Nevavuori, P., Narra, N. & Lipping, T., Crop Yield Prediction with Deep Convolutional Neural Networks, Computers and Electronics in Agriculture, 163, p. 104859, 2019.
Sharma, S., Rai, S., & Krishnan, N.C., Wheat Crop Yield Prediction Using Deep LSTM Model, arXiv preprint arXiv:2011.01498, 2020.
Khaki, S., Wang, L. & Archontoulis, S.V., A CNN-RNN Framework for Crop Yield Prediction, Frontiers in Plant Science, 10, 1750, 2020.
Elavarasan, D. & Vincent, P.D., Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications, IEEE Access, 8, pp. 86886-86901, 2020.
You, J., Li, X., Low, M., Lobell, D. & Ermon, S., Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data, in Proceedings of the AAAI Conference on Artificial Intelligence, 31(1), pp. 455-4565, 2017.
Lin, T., Guo, T. & Aberer, K., Hybrid Neural Networks for Learning the Trend in Time Series, Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 2273-2279, 2017.
Milad, A., Adwan, I., Majeed, S.A., Yusoff, N.I.M., Al-Ansari, N. & Yaseen, Z.M., Emerging Technologies of Deep Learning Models Development for Pavement Temperature Prediction, IEEE Access, 9, pp. 23840-23849, 2021.
Li, F., Liu, M. & Alzheimer?s Disease Neuroimaging Initiative, A Hybrid Convolutional and Recurrent Neural Network for Hippocampus Analysis in Alzheimer?s Disease, Journal of Neuroscience Methods, 323, pp. 108-118, 2019.
Kousik, N., Natarajan, Y., Raja, R.A., Kallam, S., Patan, R. & Gandomi, A. H., Improved Salient Object Detection Using Hybrid Convolution Recurrent Neural Network, Expert Systems with Applications, 166, 114064, 2021.
Khaki, S., Pham, H. & Wang, L., Yieldnet: A Convolutional Neural Network for Simultaneous Corn and Soybean Yield Prediction Based on Remote Sensing Data, Arxiv Preprint Arxiv:2012.03129, 2020.
India Meteorological Department of Kerala, mausam.imd.gov.in/Thiruvananthapuram (10 July 2021).
For the Rubber Research Institute of India (Rubber-board), http://rubsis.rubberboard.org.in/app/index.html?lang=en (10 July 2021).
Hu, Y., Wong, Y., Wei, W., Du, Y., Kankanhalli, M. & Geng, W.A., Novel Attention-Based Hybrid CNN-RNN Architecture for SEMG-Based Gesture Recognition, PloS One, 13(10), e0206049, 2018.
Ioffe, S., & Szegedy, C., Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, International Conference on Machine Learning, pp. 448-456, 2015.
Lipton, Z.C., Kale, D.C., Elkan, C. & Wetzell, R., Learning to Diagnose with LSTM Recurrent Neural Networks, International Conference on Learning Representations, 2016.