long short‐term memory network; multispectral; thermal infrared; UAV; wheat yield; Food Science; Agronomy and Crop Science; Plant Science
Abstract :
[en] 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 R2 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 R2 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.
Disciplines :
Computer science Agriculture & agronomy
Author, co-author :
Shen, Yulin ; Université de Liège - ULiège > TERRA Research Centre ; Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, China
Mercatoris, Benoît ; Université de Liège - ULiège > TERRA Research Centre > Biosystems Dynamics and Exchanges (BIODYNE)
Cao, Zhen; Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, China
Kwan, Paul ; Melbourne Institute of Technology, Melbourne, Australia
Guo, Leifeng; Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, China
Yao, Hongxun; School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
Cheng, Qian; Henan Key Laboratory of Water‐Saving Agriculture, Farmland Irrigation Research Institute, Chinese Academy of Agricultural Sciences, Xinxiang, China
Language :
English
Title :
Improving Wheat Yield Prediction Accuracy Using LSTM‐RF Framework Based on UAV Thermal Infrared and Multispectral Imagery
Funding: This research was funded by National Key R&D Program of China(2021ZD0110901), Sci‐ ence and Technology Planning Project of Inner Mongolia Autonomous Region(2021GG0341) and Central Public‐interest Scientific Institution Basal Research Fund (FIRI2022‐23).This research was funded by National Key R&D Program of China(2021ZD0110901), Science and Technology Planning Project of Inner Mongolia Autonomous Region(2021GG0341) and Central Public‐interest Scientific Institution Basal Research Fund (FIRI2022‐23).
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