Classification models for Tobacco Mosaic Virus and Potato Virus Y using hyperspectral and machine learning techniques

Frontiers in Plant Science

Plant Science

Leifeng Guo¹  Dongyang Liu²  Lianqiang Jiang²  Kun Huang³  Haitao Chen⁴  Xinwei Wang⁵  Yujing Han⁵  Yongchang Liu⁵  Wenxin Xue⁵  Hongtao Wang⁵  Jie Wang⁵ 

[1] Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, China;Science and Technology Department of Sichuan Liangshan Company, Liangshan Yi Autonomous Prefecture, Xichang, China;Science and Technology Department of Yunnan Honghe Company, Hani-Yi Autonomous of Honghe Prefecture, Mile, China;Tobacco Research Institute of Chongqing Company, Chongqing, China;Tobacco Research Institute, Chinese Academy of Agricultural Sciences, Qingdao, China;

关键词: precision agriculture;    virus diseases;    machine learning;    hyperspectral;    nondestructive;   

DOI  :  10.3389/fpls.2023.1211617

 received in 2023-04-27, accepted in 2023-10-03,  发布年份 2023

来源: Frontiers

【 摘 要 】

Tobacco Mosaic Virus (TMV) and Potato Virus Y (PVY) pose significant threats to crop production. Non-destructive and accurate surveillance is crucial to effective disease control. In this study, we propose the adoption of hyperspectral and machine learning technologies to discern the type and severity of tobacco leaves affected by PVY and TMV infection. Initially, we applied three preprocessing methods – Multivariate Scattering Correction (MSC), Standard Normal Variate (SNV), and Savitzky-Golay smoothing filter (SavGol) – to corrected the leaf full-length spectral sheet data (350-2500nm). Subsequently, we employed two classifiers, support vector machine (SVM) and random forest (RF), to establish supervised classification models, including binary classification models (healthy/diseased leaves or PVY/TMV infected leaves) and six-class classification models (healthy and various severity levels of diseased leaves). Based on the core evaluation index, our models achieved accuracies in the range of 91–100% in the binary classification. In general, SVM demonstrated superior performance compared to RF in distinguishing leaves infected with PVY and TMV. Different combinations of preprocessing methods and classifiers have distinct capabilities in the six-class classification. Notably, SavGol united with SVM gave an excellent performance in the identification of different PVY severity levels with 98.1% average precision, and also achieved a high recognition rate (96.2%) in the different TMV severity level classifications. The results further highlighted that the effective wavelengths captured by SVM, 700nm and 1800nm, would be valuable for estimating disease severity levels. Our study underscores the efficacy of integrating hyperspectral technology and machine learning, showcasing their potential for accurate and non-destructive monitoring of plant viral diseases.

【 授权许可】

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Copyright © 2023 Chen, Han, Liu, Liu, Jiang, Huang, Wang, Guo, Wang, Wang and Xue