Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 2 2021
Convolution and Recurrent Hybrid Neural Network for Hevea Yield Prediction

Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 2 2021
Convolution and Recurrent Hybrid Neural Network for Hevea Yield Prediction

convolutional neural networks; feature selection; recurrent neural networks; rubber yield prediction; weather prediction.

Abstract. 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.

Lince Rachel Varghese & Vanitha Kandasamy

DOI: 10.5614/itbj.ict.res.appl.2021.15.2.6

IRCS-ITB

07 Juli 2021

Sri Wahyuni

ISSN: 2337-5787

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Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 2 2021