Smartphone image dataset for radish plant leaf disease classification from Bangladesh.

Abstract

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.

Plant phenotyping relevance

ダイコン葉の病害状態を画像で取得したデータセットの構築が中心であり、植物病害フェノタイピング用の再利用可能な資源に該当する。

Code and data availability

The paper is a Data in Brief article describing a public Mendeley Data repository of 2801 smartphone images of radish leaves (healthy plus four disease classes) collected in Bangladesh, which is the paper's own phenotyping image dataset and is directly accessible.