数字农科院2.0

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

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

作者机构：

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

期刊名称： Remote Sensing

ISSN： 2072-4292

年卷期： 2024 年 16 卷 24 期

页码：

收录情况： SCIE（2024版） ; ; EI（2024版）

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

[1]ASSIMILATION OF FIELD MEASURED LAI INTO CROP GROWTH MODEL BASED ON SCE-UA OPTIMIZATION ALGORITHM. Ren, Jianqiang,Yu, Fushui,Chen, Zhongxin,Ren, Jianqiang,Yu, Fushui,Chen, Zhongxin,Du, Yunyan,Qin, Jun. 2009

[2]Threshold Microsclerotial Inoculum for Cotton Verticillium Wilt Determined Through Wet-Sieving and Real-Time Quantitative PCR. Feng Wei,Rong Fan,Haitao Dong,Wenjing Shang,Xiangming Xu,Heqin Zhu,Jiarong Yang,Xiaoping Hu.

[3]Estimating Severity Level of Cotton Infected Verticillium Wilt Based on Spectral Indices of TM Image. Chen, Bing,Wang, Keru,Li, Shaokun,Xiao, Chunhua,Chen, Jianglu,Jin, Xiulinag,Wang, Keru,Li, Shaokun,Chen, Bing.

[4]Pathogenic analysis of Borrelia garinii strain SZ isolated from northeastern China. Luo, Jianxun. 2013

[5]Recombinant pseudorabies virus expressing P12A and 3C of FMDV can partially protect piglets against FMDV challenge. Zhang, Keshan,Wang, Qingang,He, Yannan,Xu, Zhuofei,Xiang, Min,Wu, Bin,Chen, Huanchun,Zhang, Keshan,Huang, Jiong.

[6]A new method of spatialization of crop area statistical data supported by remote sensing technology. Ren, Jianqiang,Chen, Zhongxin,Chen, Zhongxin,Tang, Huajun,Liu, Xingren. 2012

[7]Genetic Algorithm-Optimized Extreme Learning Machine Model for Estimating Daily Reference Evapotranspiration in Southwest China. Quanshan Liu,Zongjun Wu,Ningbo Cui,Wenjiang Zhang,Yaosheng Wang,Xiaotao Hu,Daozhi Gong,Shunsheng Zheng. 2022

[8]Estimation of maize evapotranspiration in semi-humid regions of northern China using Penman-Monteith model and segmentally optimized Jarvis model. Zongjun Wu,Ningbo Cui,Lu Zhao,Le Han,Xiaotao Hu,Huanjie Cai,Daozhi Gong,Liwen Xing,Xi Chen,Bin Zhu,Min Lv,Shidan Zhu,Quanshan Liu. 2022

[9]Efficacy evaluation and mechanism of Bacillus subtilis EBS03 against cotton Verticillium wilt. Hongyan Bai,Zili Feng,Lihong Zhao,Hongjie Feng,Feng Wei,Jinglong Zhou,Aixing Gu,Heqin Zhu,Jun Peng,Yalin Zhang. 2022

[10]Intelligent identification on cotton verticillium wilt based on spectral and image feature fusion. Zhihao Lu,Shihao Huang,Xiaojun Zhang,Yuxuan shi,Wanneng Yang,Longfu Zhu,Chenglong Huang. 2023

[11]Attention-optimized DeepLab V3+for automatic estimation of cucumber disease severity. Li, Kaiyu,Zhang, Lingxian,Li, Bo,Li, Shufei,Ma, Juncheng. 2022

[12]CVW-Etr: A High-Precision Method for Estimating the Severity Level of Cotton Verticillium Wilt Disease. Pan Pan,Qiong Yao,Jiawei Shen,Lin Hu,Sijian Zhao,Longyu Huang,Guoping Yu,Guomin Zhou,Jianhua Zhang. 2024

[13]An Adaptive Spiral Strategy Dung Beetle Optimization Algorithm: Research and Applications. Xiong Wang,Yi Zhang,Changbo Zheng,Shuwan Feng,Hui Yu,Bin Hu,Zihan Xie. 2024

[14]A comprehensive review on elucidating the host disease resistance mechanism from the perspective of the interaction between cotton and Verticillium dahliae. Zhang, Yalin,Zhao, Lihong,Li, Dongpo,Li, Ziming,Feng, Hongjie,Feng, Zili,Wei, Feng,Zhou, Jinglong,Ma, Zhiying,Yang, Jun,Zhu, Heqin. 2025

[15]A comprehensive review on elucidating the host disease resistance mechanism from the perspective of the interaction between cotton and Verticillium dahliae. . 2025

[16]A Phenology-Based Spectral And Temporal F.eature Selection Method For C rop Mapping From Satellite Time Series. Hu, Q, Sulla, D, Xu, BD, Yin, H, Tang, HJ, Yang, P, Wu, WB. 2019

[17]Soil Organic Carbon Prediction Based on Different Combinations of Hyperspectral Feature Selection and Regression Algorithms. Chang, Naijie,Jing, Xiaowen,Zeng, Wenlong,Zhang, Yungui,Li, Zhihong,Chen, Di,Jiang, Daibing,Zhong, Xiaoli,Dong, Guiquan,Liu, Qingli. 2023

[18]A Classification Feature Optimization Method for Remote Sensing Imagery Based on Fisher Score and mRMR. Lv, Chengzhe,Lu, Yuefeng,Lu, Miao,Feng, Xinyi,Fan, Huadan,Xu, Changqing,Xu, Lei. 2022

[19]Combining novel feature selection strategy and hyperspectral vegetation indices to predict crop yield. Fei S.,Li L.,Han Z.,Chen Z.,Xiao Y.. 2022

[20]UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat. Zongpeng Li,Zhen Chen,Qian Cheng,Fuyi Duan,Ruixiu Sui,Xiuqiao Huang,Honggang Xu. 2022

作者其他论文 更多>>