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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Che-Hao Chang & Jason Lin & Jia-Wei Chang & Yu-Shun Huang & Ming-Hsin Lai & Yen-Jen Chang, 2024. "Hybrid Deep Neural Networks with Multi-Tasking for Rice Yield Prediction Using Remote Sensing Data," Agriculture, MDPI, vol. 14(4), pages 1-21, March.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:4:p:513-:d:1362204
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    References listed on IDEAS

    as
    1. Ekaansh Khosla & Ramesh Dharavath & Rashmi Priya, 2020. "Crop yield prediction using aggregated rainfall-based modular artificial neural networks and support vector regression," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(6), pages 5687-5708, August.
    2. Papadopoulos, Sokratis & Kontokosta, Constantine E., 2019. "Grading buildings on energy performance using city benchmarking data," Applied Energy, Elsevier, vol. 233, pages 244-253.
    3. Yu, Xinran & Ergan, Semiha & Dedemen, Gokmen, 2019. "A data-driven approach to extract operational signatures of HVAC systems and analyze impact on electricity consumption," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    4. Jig Han Jeong & Jonathan P Resop & Nathaniel D Mueller & David H Fleisher & Kyungdahm Yun & Ethan E Butler & Dennis J Timlin & Kyo-Moon Shim & James S Gerber & Vangimalla R Reddy & Soo-Hyung Kim, 2016. "Random Forests for Global and Regional Crop Yield Predictions," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-15, June.
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