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Improving Wheat Yield Prediction Accuracy Using LSTM-RF Framework Based on UAV Thermal Infrared and Multispectral Imagery

Author

Listed:
  • Yulin Shen

    (Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    Biosystems Dynamics and Exchanges, Gembloux Agro-Bio Tech, TERRA Teaching and Research Centre, University of Liège, 5030 Gembloux, Belgium)

  • Benoît Mercatoris

    (Biosystems Dynamics and Exchanges, Gembloux Agro-Bio Tech, TERRA Teaching and Research Centre, University of Liège, 5030 Gembloux, Belgium)

  • Zhen Cao

    (Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China)

  • Paul Kwan

    (Melbourne Institute of Technology, The Argus, 288 La Trobe St., Melbourne, VIC 3000, Australia)

  • Leifeng Guo

    (Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China)

  • Hongxun Yao

    (School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China)

  • Qian Cheng

    (Henan Key Laboratory of Water-Saving Agriculture, Farmland Irrigation Research Institute, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China)

Abstract

Yield prediction is of great significance in agricultural production. Remote sensing technology based on unmanned aerial vehicles (UAVs) offers the capacity of non-intrusive crop yield prediction with low cost and high throughput. In this study, a winter wheat field experiment with three levels of irrigation (T1 = 240 mm, T2 = 190 mm, T3 = 145 mm) was conducted in Henan province. Multispectral vegetation indices (VIs) and canopy water stress indices (CWSI) were obtained using an UAV equipped with multispectral and thermal infrared cameras. A framework combining a long short-term memory neural network and random forest (LSTM-RF) was proposed for predicting wheat yield using VIs and CWSI from multi-growth stages as predictors. Validation results showed that the R 2 of 0.61 and the RMSE value of 878.98 kg/ha was achieved in predicting grain yield using LSTM. LSTM-RF model obtained better prediction results compared to the LSTM with n R 2 of 0.78 and RMSE of 684.1 kg/ha, which is equivalent to a 22% reduction in RMSE. The results showed that LSTM-RF considered both the time-series characteristics of the winter wheat growth process and the non-linear characteristics between remote sensing data and crop yield data, providing an alternative for accurate yield prediction in modern agricultural management.

Suggested Citation

  • Yulin Shen & Benoît Mercatoris & Zhen Cao & Paul Kwan & Leifeng Guo & Hongxun Yao & Qian Cheng, 2022. "Improving Wheat Yield Prediction Accuracy Using LSTM-RF Framework Based on UAV Thermal Infrared and Multispectral Imagery," Agriculture, MDPI, vol. 12(6), pages 1-13, June.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:6:p:892-:d:843392
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    References listed on IDEAS

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    1. Shao, Guomin & Han, Wenting & Zhang, Huihui & Liu, Shouyang & Wang, Yi & Zhang, Liyuan & Cui, Xin, 2021. "Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices," Agricultural Water Management, Elsevier, vol. 252(C).
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    Cited by:

    1. Zhonglin Ji & Yaozhong Pan & Xiufang Zhu & Dujuan Zhang & Jiajia Dai, 2022. "Prediction of Corn Yield in the USA Corn Belt Using Satellite Data and Machine Learning: From an Evapotranspiration Perspective," Agriculture, MDPI, vol. 12(8), pages 1-23, August.
    2. Chin-Hung Kuan & Yungho Leu & Wen-Shin Lin & Chien-Pang Lee, 2022. "The Estimation of the Long-Term Agricultural Output with a Robust Machine Learning Prediction Model," Agriculture, MDPI, vol. 12(8), pages 1-15, July.

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