A deep learning model for rapid classification of tea coal disease

作者：Yang Xu, Yilin Mao, Li He, Litao Sun, Shuangshuang Wang, Xiaojiang Li, Jiazhi Shen, Xinyue Yin, Kai Fan, Zhaotang Ding, Yu Wang · 发表于：Plant Methods · 年份：2023 · DOI：10.1186/s13007-023-01074-2 · 被引用次数：40 · 研究领域：Spectroscopy and Chemometric Analyses、Remote Sensing in Agriculture、Smart Agriculture and AI

BACKGROUND: The 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. RESULTS: Both 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. CONCLUSIONS: This 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.