Tobacco Plant Disease Dataset

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationFourteenth International Conference on Digital Image Processing, ICDIP 2022
EditorsXudong Jiang, Wenbing Tao, Deze Zeng, Yi Xie
ISBN (Electronic)9781510657564
Publication statusPublished - 2022
Event14th International Conference on Digital Image Processing, ICDIP 2022 - Wuhan, China
Duration: 20 May 202223 May 2022

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


Conference14th International Conference on Digital Image Processing, ICDIP 2022


  • Tobacco
  • computer vision
  • dataset
  • deep learning
  • leaf detection
  • plant disease detection


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