ArticlePlant methods2023
A deep learning model for rapid classification of tea coal disease.
Article in Plant methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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13 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
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- MAF-MixNet: Few-Shot Tea Disease Detection Based on Mixed Attention and Multi-Path Feature Fusion.Plants (Basel, Switzerland) · 2025Article
- DWTFormer: a frequency-spatial features fusion model for tomato leaf disease identification.Plant methods · 2025Article
- Detection techniques for tomato diseases under non-stationary climatic conditions.Frontiers in plant science · 2025Article
- Deep learning-based automated detection of endometrioid endometrial carcinoma in histopathology.Frontiers in oncology · 2025Article
- Integrative hyperspectral, transcriptomic, and metabolomic analysis reveals the mechanism of tea plants in response to sooty mold disease.BMC plant biology · 2024Article
- A Monitoring Device and Grade Prediction System for Grain Mildew.Sensors (Basel, Switzerland) · 2024Article
- Cucumber pathogenic spores' detection using the GCS-YOLOv8 network with microscopic images in natural scenes.Plant methods · 2024Article
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Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
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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