Hybrid Deep Neural Networks with Multi-Tasking for Rice Yield Prediction Using Remote Sensing Data

Che-Hao Chang, Jason Lin (), Jia-Wei Chang, Yu-Shun Huang, Ming-Hsin Lai and Yen-Jen Chang
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Che-Hao Chang: Department of Computer Science and Engineering, National Chung Hsing University, No. 145, Xingda Rd., South District, Taichung 40227, Taiwan
Jason Lin: Department of Computer Science and Engineering, National Chung Hsing University, No. 145, Xingda Rd., South District, Taichung 40227, Taiwan
Jia-Wei Chang: Department of Computer Science and Engineering, National Chung Hsing University, No. 145, Xingda Rd., South District, Taichung 40227, Taiwan
Yu-Shun Huang: Crop Science Division, Agricultural Research Institute, Council of Agriculture, Executive Yuan, Taichung 41362, Taiwan
Ming-Hsin Lai: Crop Science Division, Agricultural Research Institute, Council of Agriculture, Executive Yuan, Taichung 41362, Taiwan
Yen-Jen Chang: Department of Computer Science and Engineering, National Chung Hsing University, No. 145, Xingda Rd., South District, Taichung 40227, Taiwan

Agriculture, 2024, vol. 14, issue 4, 1-21

Abstract: Recently, data-driven approaches have become the dominant solution for prediction problems in agricultural industries. Several deep learning models have been applied to crop yield prediction in smart farming. In this paper, we proposed an efficient hybrid deep learning model that coordinates the outcomes of a classification model and a regression model in deep learning via the shared layers to predict the rice crop yield. Three statistical analyses on the features, including Pearson correlation coefficients (PCC), Shapley additive explanations (SHAP), and recursive feature elimination with cross-validation (RFECV), are proposed to select the most relevant ones for the predictive goal to reduce the model training time. The data preprocessing normalizes the features of the collected data into specific ranges of values and then reformats them into a three-dimensional matrix. As a result, the root-mean-square error (RMSE) of the proposed model in rice yield prediction has achieved 344.56 and an R-squared of 0.64. The overall performance of the proposed model is better than the other deep learning models, such as the multi-parametric deep neural networks (MDNNs) (i.e., RMSE = 370.80, R-squared = 0.59) and the artificial neural networks (ANNs) (i.e., RMSE = 550.03, R-squared = 0.09). The proposed model has demonstrated significant improvement in the predictive results of distinguishing high yield from low yield with 90% accuracy and 94% F1 score.

Keywords: crop yield prediction; remote sensing; convolutional neural network; deep learning; multi-task learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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