卷积递归混合神经网络在橡胶产量预测中的应用

IF 0.5 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of ICT Research and Applications Pub Date : 2021-10-19 DOI:10.5614/itbj.ict.res.appl.2021.15.2.6
L. Varghese, Vanitha Kandasamy
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引用次数: 1

摘要

深度学习技术已被有效地用于橡胶作物产量预测。卷积神经网络(CNN)和递归神经网络(RNN)的混合预测可以有效地处理特征的不确定性,是作物产量预测的最佳技术。因此,本文提出了一种基于印度喀拉拉邦环境数据的混合CNN-RNN方法来预测橡胶树产量。提出的混合CNN-RNN方法通过批归一化减少了CNN的内部协变量移位,并利用具有细胞激活机制的LSTM解决了RNN的梯度消失或爆炸问题。该方法具有三个基本特点:(1)捕获了环境因素的时间依赖性,提高了固有的计算时间;(ii)能够在不确定条件下推广产量预测而不损失预测精度;(iii)与反向传播和前馈方法相结合,可以揭示天气条件和土壤数据条件的样本适合的程度,从而提供橡胶产量变化之间的明确边界。
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Convolution and Recurrent Hybrid Neural Network for Hevea Yield 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.
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来源期刊
Journal of ICT Research and Applications
Journal of ICT Research and Applications COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
1.60
自引率
0.00%
发文量
13
审稿时长
24 weeks
期刊介绍: Journal of ICT Research and Applications welcomes full research articles in the area of Information and Communication Technology from the following subject areas: Information Theory, Signal Processing, Electronics, Computer Network, Telecommunication, Wireless & Mobile Computing, Internet Technology, Multimedia, Software Engineering, Computer Science, Information System and Knowledge Management. Authors are invited to submit articles that have not been published previously and are not under consideration elsewhere.
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