孟加拉国萝卜叶片病害分类的智能手机图像数据集。

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES Data in Brief Pub Date : 2025-02-01 Epub Date: 2024-12-27 DOI:10.1016/j.dib.2024.111263
Mahamudul Hasan, Raiyan Gani, Mohammad Rifat Ahmmad Rashid, Maherun Nessa Isty, Raka Kamara, Taslima Khan Tarin
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引用次数: 0

摘要

萝卜是常见的根茎类蔬菜,富含维生素和矿物质,热量低。这种蔬菜以生长迅速而闻名。然而,各种各样的叶片病害,即叶片受到各种细菌和真菌病害的影响,会阻碍萝卜的健康生长。此外,如果农民试图使用传统方法来识别这些疾病,则存在不准确识别疾病的高风险。为了准确识别萝卜叶片病害,使萝卜生长最好,在孟加拉国菜地共收集了2801张萝卜叶片图像。收集的数据集包括健康叶片的综合图像,以及四种受各种疾病影响的叶片,如黑叶斑病、唐尼霉病、跳蚤甲虫和花叶病。利用该稳健的数据集,可以训练深度学习模型来识别叶片病害,从而有助于检测病害,从而减少萝卜种植的危害。通过准确识别萝卜叶片上的疾病并保持萝卜的健康生产,该数据集有助于农业部门更广泛的可持续性。
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Smartphone image dataset for radish plant leaf disease classification from Bangladesh
Radishes, which are common root vegetables, are rich in vitamins and minerals, and contain low calories. This vegetable is known for its rapid growth. Nevertheless, the variety of leaf diseases where leaves get affected by various bacterial and fungal diseases can hinder the healthy growth of radish. Furthermore, there is a high risk of inaccurate identification of diseases if the farmers try to use traditional methods in recognizing these diseases. With the purpose of precise identification of radish leaf diseases for the finest growth of this vegetable, total of 2801 images of the radish leaves are collected from vegetable field in Bangladesh. The collected dataset includes comprehensive images of healthy leaves as well as four types of leaf affected by various diseases such as Black Leaf Spot, Downey Mildew, Flea Beetle and Mosaic. Utilizing this robust dataset, deep learning models can be trained to identify the leaf diseases which helps to detect the diseases in order to reduce the harm of the cultivation of radish. By identifying the diseases on radish leaves accurat-ely and maintaining healthy production of radish, this dataset contributes to the broader sustainability in the agricultural sector.
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来源期刊
Data in Brief
Data in Brief MULTIDISCIPLINARY SCIENCES-
CiteScore
3.10
自引率
0.00%
发文量
996
审稿时长
70 days
期刊介绍: Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.
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