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

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

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 labo...

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Main Authors: Xuchun Li, Jixuan Yan, Caixia Huang, Weiwei Ma, Zichen Guo, Jie Li, Xiangdong Yao, Qihong Da, Kejing Cheng, Hongyan Yang
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Agriculture
Subjects:
precision agriculture
remote sensing
machine learning fusion
vegetation indices
crop monitoring
Online Access:https://www.mdpi.com/2077-0472/15/7/746
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Summary: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<sup>2</sup>) 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<sup>2</sup> 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.
ISSN:2077-0472

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