Ms. hong lin Profile

hong lin

at Macao Polytechnic University

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Proceedings Article | 12 October 2022 Paper

Tobacco plant disease dataset

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

Proceedings Volume 12342, 123423Q (2022) https://doi.org/10.1117/12.2644288

KEYWORDS: Image segmentation, Pathogens, Image classification, Pathology, Computer science, Agriculture, Visual process modeling, Target detection

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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.

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