Estimation of Silage Maize Plant Moisture Content Based on UAV Multispectral Data and Ensemble Learning Methods

Xuchun Li, Jixuan Yan (), Caixia Huang, Weiwei Ma, Zichen Guo, Jie Li, Xiangdong Yao, Qihong Da, Kejing Cheng and Hongyan Yang
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Xuchun Li: College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China
Jixuan Yan: College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China
Caixia Huang: College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China
Weiwei Ma: State Key Laboratory of Crop Science in Arid Habitat Co-Constructed by Province and Ministry, Lanzhou 730070, China
Zichen Guo: College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China
Jie Li: College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China
Xiangdong Yao: College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China
Qihong Da: College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China
Kejing Cheng: College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China
Hongyan Yang: College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China

Agriculture, 2025, vol. 15, issue 7, 1-18

Abstract: Plant moisture content (PMC) serves as a crucial indicator of crop water status, directly affecting agricultural productivity, product quality, and the effectiveness of precision irrigation. Conventional methods for PMC assessment predominantly rely on destructive sampling techniques, which are labor-intensive and impede real-time monitoring. This study investigates silage maize cultivated in the Hexi region of China, leveraging multispectral data acquired via an unmanned aerial vehicle (UAV) to estimate PMC across different phenological stages. A stacked ensemble learning framework was developed, integrating Back Propagation Neural Network (BPNN), Random Forest Regression (RFR), and Support Vector Regression (SVR), with Partial Least Squares Regression (PLSR) employed for feature fusion. The findings indicate that incorporating vegetation indices into spectral variables significantly improved prediction performance. The standalone models demonstrated coefficient of determination (R 2 ) values ranging from 0.43 to 0.69, with root mean square error (RMSE) spanning 0.61% to 1.43%. In contrast, the ensemble model exhibited superior accuracy, achieving R 2 values between 0.61 and 0.87 and RMSE values from 0.54% to 1.38%. This methodology offers a scalable, non-invasive alternative for PMC estimation, facilitating data-driven irrigation optimization in regions facing water scarcity.

Keywords: precision agriculture; remote sensing; machine learning fusion; vegetation indices; crop monitoring (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: 2025
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