NDLI: Prediction of Rice Yield in East China Based on Climate and Agronomic Traits Data Using Artificial Neural Networks and Partial Least Squares Regression

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Prediction of Rice Yield in East China Based on Climate and Agronomic Traits Data Using Artificial Neural Networks and Partial Least Squares Regression

Content Provider	MDPI
Author	Guo, Yuming Xiang, Haitao Li, Zhenwang Ma, Fei Du, Changwen
Copyright Year	2021
Description	Rice yield is not only influenced by factors of varieties and managements, but also by environmental factors. In this study, agronomic trait data of rice and climate data in eastern China were collected, and rice yields were predicted using a variety of algorithms, including the non-linear tool of feed-forward backpropagation neural networks (FFBN) and the linear model of partial least squares regression (PLSR). The results showed that both the agronomic traits and the climate data were significantly related with rice yield. The PLSR model showed that covariates occurred among the parameters, and modifications should be considered for climate data-based modelling. The FFBN model demonstrated better prediction performance than that of PLSR, in which the relation coefficient (R2) and root mean square error (RMSE) were 0.611 vs. 0.374 and 0.578 vs. 0.865 ton/ha using climate data, respectively; and 0.742 vs. 0.689 and 0.556 vs. 0.608 using agronomic trait data, respectively. When using fused data the R2 and RMSE improved to 0.843 vs. 0.746 and 0.440 vs. 0.549, respectively. The optimum architecture of the FFBN consisted of one hidden layer with 29 neurons. Therefore, the FFBN algorithm is an effective option for the prediction of rice yield in complex systems of rice production.
Starting Page	282
e-ISSN	20734395
DOI	10.3390/agronomy11020282
Journal	Agronomy
Issue Number	2
Volume Number	11
Language	English
Publisher	MDPI
Publisher Date	2021-02-03
Access Restriction	Open
Subject Keyword	Agronomy Agricultural Engineering Water Science and Technology Rice Yield Artificial Neural Network Partial Least Squares Regression Climate Data Agronomic Traits
Content Type	Text
Resource Type	Article

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