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A deep learning model for rapid classification of tea coal disease

文献类型： 外文期刊

作者： Xu, Yang 1 ; Mao, Yilin 1 ; Li, He 1 ; Sun, Litao 2 ; Wang, Shuangshuang 2 ; Li, Xiaojiang 2 ; Shen, Jiazhi 2 ; Yin, Xinyue 1 ; Fan, Kai 1 ; Ding, Zhaotang 2 ; Wang, Yu 1 ;

作者机构： 1.Qingdao Agr Univ, Tea Res Inst, Qingdao 266109, Peoples R China

2.Shandong Acad Agr Sci, Tea Res Inst, Jinan 250100, Peoples R China

关键词： Tea coal disease; RGB; Hyperspectral; Machine learning; Deep learning; Classification

期刊名称：PLANT METHODS （ 影响因子：5.1； 五年影响因子：6.1 ）

ISSN：

年卷期： 2023 年 19 卷 1 期

页码：

收录情况： SCI

摘要： BackgroundThe common tea tree disease known as "tea coal disease" (Neocapnodium theae Hara) can have a negative impact on tea yield and quality. The majority of conventional approaches for identifying tea coal disease rely on observation with the human naked eye, which is labor- and time-intensive and frequently influenced by subjective factors. The present study developed a deep learning model based on RGB and hyperspectral images for tea coal disease rapid classification.ResultsBoth RGB and hyperspectral could be used for classifying tea coal disease. The accuracy of the classification models established by RGB imaging using ResNet18, VGG16, AlexNet, WT-ResNet18, WT-VGG16, and WT-AlexNet was 60%, 58%, 52%, 70%, 64%, and 57%, respectively, and the optimal classification model for RGB was the WT-ResNet18. The accuracy of the classification models established by hyperspectral imaging using UVE-LSTM, CARS-LSTM, NONE-LSTM, UVE-SVM, CARS-SVM, and NONE-SVM was 80%, 95%, 90%, 61%, 77%, and 65%, respectively, and the optimal classification model for hyperspectral was the CARS-LSTM, which was superior to the model based on RGB imaging.ConclusionsThis study revealed the classification potential of tea coal disease based on RGB and hyperspectral imaging, which can provide an accurate, non-destructive, and efficient classification method for monitoring tea coal disease.

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