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| Research on the Inversion of Moisture Content in Rapeseed Silique Peel Based on Hyperspectral Fusion Imaging |
| WEI Wei1, WANG Dan2, WANG Bo-tao3, TAN Zuo-jun1, LIU Quan1, XIE Jing1* |
1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
2. College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
3. College of Resources & Environment, Huazhong Agricultural University, Wuhan 430070, China
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Abstract To explore the potential of indirectly estimating the moisture content in silique peel based on hyperspectral data, this study took the rapeseed experimental field as the research object. From March to May 2023, the rapeseed spectra and moisture content of rapeseed silique peel were collected from the experimental field. After two spectral preprocessing methods, three feature wavelength selection methods, and their combinations, hyperspectral image spatial texture information was introduced. Partial Least Squares Regression (PLSR), Lasso regression, Support Vector Regression (SVR), and Extreme Learning Machine (ELM) were used to establish a regression model for the moisture content of rapeseed silique peel, and the accuracy of the model results was evaluated. The research results indicate that: (1) Spectral preprocessing techniques can highlight some hidden information in the spectrum, and mathematical transformations such as Multiple Scatter Correction (MSC) and First Derivative (FD) are more conducive to extracting spectral sensitive information; (2) After performing preprocessing, a feature selecting method combining Competitive Adaptive Reweighted Sampling (CARS) and Iterative Retention Informative Variables (IRIV) was used. The Lasso model demonstrated the best prediction performance, with an R2 of 0.772 0 for test set 3.In response to the complex structure, small volume, and geometric influence of moisture content distribution in rapeseed silique peel, spatial texture information is introduced based on pure spectral information. Spatial texture quantifies the spatial variation and structural details (such as wrinkles and bumps) related to moisture content on the surface of silique, compensates for the variation caused by the shape and orientation of silique in a single pixel spectrum, improves the regression accuracy and prediction ability of the model, enhances the robustness of the model to noise and outliers, and provides a new effective way to solve the precise inversion of physiological parameters of complex small-scale crop objects.
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Received: 2024-12-04
Accepted: 2025-07-10
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Corresponding Authors:
XIE Jing
E-mail: xiejing625@mail.hzau.edu.cn
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