Severity Assessment of Cotton Canopy Verticillium Wilt by Machine Learning Based on Feature Selection and Optimization Algorithm Using UAV Hyperspectral Data

文献类型： 外文期刊

第一作者： Li, Weinan

作者： Li, Weinan;Guo, Yang;Yang, Weiguang;Lan, Yubin;Li, Weinan;Guo, Yang;Yang, Weiguang;Lan, Yubin;Li, Weinan;Huang, Longyu;Zhang, Jianhua;Peng, Jun;Huang, Longyu;Peng, Jun;Zhang, Jianhua

作者机构：

关键词： cotton Verticillium wilt; unmanned aerial vehicle; hyperspectral imaging; feature selection; optimization algorithm; disease severity

期刊名称：REMOTE SENSING （ 影响因子：4.1； 五年影响因子：4.8 ）

ISSN：

年卷期： 2024 年 16 卷 24 期

页码：

收录情况： SCI

摘要： Verticillium wilt (VW) represents the most formidable challenge in cotton cultivation, critically impairing both fiber yield and quality. Conventional resistance assessment techniques, which are largely reliant on subjective manual evaluation, fail to meet the demands for precision and scalability required for advanced genetic research. This study introduces a robust evaluation framework utilizing feature selection and optimization algorithms to enhance the accuracy and efficiency of the severity assessment of cotton VW. We conducted comprehensive time-series UAV hyperspectral imaging (400 to 995 nm) on the cotton canopy in a field environment on different days after sowing (DAS). After preprocessing the hyperspectral data to extract wavelet coefficients and vegetation indices, various feature selection methods were implemented to select sensitive spectral features for cotton VW. By leveraging these selected features, we developed machine learning models to assess the severity of cotton VW at the canopy scale. Model validation revealed that the performance of the assessment models responded dynamically as VW progressed and achieved the highest R2 of 0.5807 at DAS 80, with an RMSE of 6.0887. Optimization algorithms made a marked improvement for SVM in severity assessment using all observation data, with R2 increasing from 0.6986 to 0.9007. This study demonstrates the potential of feature selection and machine learning methods based on hyperspectral data in enhancing VW management, promising advancements in high-throughput automated disease assessment, and supporting sustainable agricultural practices.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序