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Attention Mechanism-Combined LSTM for Grain Yield Prediction in China Using Multi-Source Satellite Imagery

Author

Listed:
  • Fan Liu

    (College of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha 410018, China)

  • Xiangtao Jiang

    (College of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha 410018, China)

  • Zhenyu Wu

    (College of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha 410018, China)

Abstract

Grain yield prediction affects policy making in various aspects such as agricultural production planning, food security assurance, and adjustment of foreign trade. Accurately predicting grain yield is of great significance in ensuring global food security. This paper is based on the MODIS remote sensing image data products from 2010 to 2020, and adds band information such as vegetation index and temperature to form composite remote sensing data as a dataset. Aiming at the lack of models for large-scale forecasting and the need for human intervention in traditional models, this paper proposes a grain production estimation model based on deep learning. First, image cropping and yield mapping techniques are used to process the data to generate training samples. Then the channel and spatial attention mechanism (convolutional block attention module, CBAM) is added to extract spatial information in different remote sensing bands to improve the efficiency of the model. Long short-term memory (LSTM) neural networks are added to obtain feature information in the time dimension. Finally, a national-scale grain yield prediction model is constructed. After the study, it was found that the LSTM model using a combination of multi-source satellite images and an attention mechanism can effectively predict grain yield in China. Furthermore, the proposed model was tested on data from 2018 to 2020 showing an average R 2 of 0.940 and an average RMSE of 80,020 tons, indicating that it can predict Chinese grain yield better. The model proposed in this paper extracts grain yield information directly from the composite remote sensing data, and solves the problem of small-scale research and imprecise yield prediction in an end-to-end manner.

Suggested Citation

  • Fan Liu & Xiangtao Jiang & Zhenyu Wu, 2023. "Attention Mechanism-Combined LSTM for Grain Yield Prediction in China Using Multi-Source Satellite Imagery," Sustainability, MDPI, vol. 15(12), pages 1-20, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9210-:d:1165610
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    References listed on IDEAS

    as
    1. Hengli Wang & Hong Liu & Rui Ma, 2022. "Assessment and Prediction of Grain Production Considering Climate Change and Air Pollution in China," Sustainability, MDPI, vol. 14(15), pages 1-22, July.
    2. Wang, Jieyong & Zhang, Ziwen & Liu, Yansui, 2018. "Spatial shifts in grain production increases in China and implications for food security," Land Use Policy, Elsevier, vol. 74(C), pages 204-213.
    3. Aizhi Yu & Entai Cai & Min Yang & Zhishan Li, 2022. "An Analysis of Water Use Efficiency of Staple Grain Productions in China: Based on the Crop Water Footprints at Provincial Level," Sustainability, MDPI, vol. 14(11), pages 1-23, May.
    4. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2021. "Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data," Land, MDPI, vol. 10(6), pages 1-21, June.
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