Author:
Xu Yang,Mao Yilin,Li He,Sun Litao,Wang Shuangshuang,Li Xiaojiang,Shen Jiazhi,Yin Xinyue,Fan Kai,Ding Zhaotang,Wang Yu
Abstract
Abstract
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.
Funder
Special Foundation for Distinguished Taishan Scholar of Shandong Province
Livelihood Project of Qingdao City
Special Talent Program of SAAS
Agricultural Improved Variety Project of Shandong Province
Technology System of Modern Agricultural Industry in Shandong Province
Publisher
Springer Science and Business Media LLC
Subject
Plant Science,Genetics,Biotechnology
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