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Long Short-Term Memory Neural Network with Attention Mechanism for Rice Yield Early Estimation in Qian Gorlos County, Northeast China

Author

Listed:
  • Jian Li

    (College of Information Technology, Jilin Agricultural University, Changchun 130118, China
    These authors contributed equally to this work.)

  • Yichen Xie

    (College of Information Technology, Jilin Agricultural University, Changchun 130118, China
    State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
    These authors contributed equally to this work.)

  • Lushi Liu

    (State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China)

  • Kaishan Song

    (State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China)

  • Bingxue Zhu

    (State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China)

Abstract

Rice is one of the most extensively cultivated food crops in Northeast China. Estimating pre-harvest rice yield is important for accurately formulating field management strategies and swiftly assessing overall rice production. This can be achieved using a pixel-scale model, which estimates crop yield based on information from each pixel. Previous studies predominantly employed remote sensing indices, climatic data, and yield statistics of rice across either single or all growth periods for yield estimation. These approaches are limited by a delay in yield estimation and are insufficient in the exploration of time-series feature variables at the pixel scale. This study presents the development of a novel deep-learning framework designed for the early estimation of rice yield in Qian Gorlos County, Northeast China. The framework utilizes a long short-term memory neural network integrated with an attention mechanism (ALSTM). In this framework, the heading stage–milk ripening stage is the time window for early yield estimation, and the vegetation indices Normalized Difference Red Edge (NDRE), Green Chlorophyll Vegetation Index (GCVI), and Normalized Difference Water Index (NDWI) from the rice transplanting to the milk ripening stage are time-series feature variables. The yield estimation precision is R 2 = 0.88, RMSE = 341.82 kg/ha, MAE = 280.29 kg/ha, outperforming LASSO (R 2 = 0.71, RMSE = 567.10 kg/ha, MAE = 487.38 kg/ha), RF (R 2 = 0.79, RMSE = 506.70 kg/ha, MAE = 418.90 kg/ha), and LSTM (R 2 = 0.83, RMSE = 451.11 kg/ha, MAE = 326.31 kg/ha). The ALSTM introduced in this paper demonstrates its robustness after being generalized to the 2022 growing season. It can forecast the current-year rice yield two months prior to harvest, providing a valuable reference for developing field management strategies to enhance rice productivity.

Suggested Citation

  • Jian Li & Yichen Xie & Lushi Liu & Kaishan Song & Bingxue Zhu, 2025. "Long Short-Term Memory Neural Network with Attention Mechanism for Rice Yield Early Estimation in Qian Gorlos County, Northeast China," Agriculture, MDPI, vol. 15(3), pages 1-21, January.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:3:p:231-:d:1572871
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    References listed on IDEAS

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    1. Muhammet Fatih Aslan & Kadir Sabanci & Busra Aslan, 2024. "Artificial Intelligence Techniques in Crop Yield Estimation Based on Sentinel-2 Data: A Comprehensive Survey," Sustainability, MDPI, vol. 16(18), pages 1-23, September.
    2. 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.
    3. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
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