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

Yulin Shen, Benoît Mercatoris, Zhen Cao, Paul Kwan, Leifeng Guo, Hongxun Yao and Qian Cheng
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Yulin Shen: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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

Agriculture, 2022, vol. 12, issue 6, 1-13

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.

Keywords: UAV; wheat yield; multispectral; thermal infrared; long short-term memory network (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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