A deep learning model for rapid classification of tea coal disease.

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.

Plant phenotyping relevance

RGB・ハイパースペクトル画像と深層学習により茶樹病害を分類する手法を開発・評価しており、感染植物の病害状態を観測する方法が研究の中心である。

Code and data availability

The supplied blocks describe RGB and hyperspectral image collection and deep learning classification of tea coal disease, but contain no public dataset deposit, no author code/workflow URL, no trained model release, and no data availability statement. No paper-specific public asset is actionable.

No evidence-backed public reproduction asset is currently recorded.