ArticleData in brief2025
Applying the transfer learning models on the dataset on the effect of diseases on Nagvel-betel (Piper betle) leaves.
Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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9 authors.
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Abstract
The dataset of Betel leaves includes 4156 leaves affected by various diseases. These diseases include Leaf Spot, Powdery Mildew, Anthracnose, Bacterial Blight, Cercospora Leaf Spot, Sooty Mold, Downy Mildew, Wilt Disease, Rust Disease, Mosaic Virus, Black Rot, Root Rot, Stem Canker, Leaf Curl Disease, and Fusarium Wilt. The camera is used to collect high-resolution images to ensure the exact detection of the images to detect diseases. The resolution of the photos was 3000 × 4000, consuming approximately 3 mb. The data set covers a wide range of diseases, and many samples were collected under each category. The dataset is saved using a hierarchical data structure, as the name of the folder indicates the label or category of the image. The reuse and recreation of this type of dataset are ensured by mapping the name of the disease with the apparent characters of the disease on the leaves. The experiment was performed using Vision Transformer Models to check the robustness of the dataset. The result of the classification report states that the range of accuracy varies from 0.7 to 0.9.
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