Tobacco plant disease dataset

Hong Lin; Rita Tse; Su-Kit Tang; Zhenping Qiang; Jinliang Ou; Giovanni Pau

doi:10.1117/12.2644288

12 October 2022 Tobacco plant disease dataset

Hong Lin, Rita Tse, Su-Kit Tang, Zhenping Qiang, Jinliang Ou, Giovanni Pau

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123423Q (2022) https://doi.org/10.1117/12.2644288
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

Tobacco is a valuable plant in agricultural and commercial industry. Any disease infection to the plant may lower the harvest and interfere the operation of supply chain in the market. Image-based deep learning methods are cutting-edge technologies that can facilitate the diagnosis of diseases efficiently and effectively when large-scale dataset is available for training. However, there is not a public dataset about tobacco currently. A comprehensive dataset is appealed to take advantage of deep learning methods in tobacco cultivation urgently. In this paper, we propose to create a specific dataset for tobacco diseases, called Tobacco Plant Disease Dataset (TPDD). 2721 tobacco leaf images are taken in field. The dataset serves for two purposes: disease classification and leaf detection. For classification, we identify 12 classes and provide two types of disease annotations: 1) Whole Leaf Section; 2) Disease Fragment Section. For leaf detection, we provide two kinds of bounding box: rectangle bounding box and polygon bounding box. In addition, we conduct baseline experiments to illustrate the usefulness of TPDD: 1) using deep learning model to detect single disease and multiple diseases; 2) using YOLO-v3 and Mask-RCNN to detect leaves. We hope that the dataset could support the tobacco industry, also be a benchmark in fine-grained vision classification.

Citation Download Citation

Hong Lin, Rita Tse, Su-Kit Tang, Zhenping Qiang, Jinliang Ou, and Giovanni Pau "Tobacco plant disease dataset", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123423Q (12 October 2022); https://doi.org/10.1117/12.2644288

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KEYWORDS

Image segmentation

Image classification

Pathogens

Agriculture

Computer science

Pathology

Target detection

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