Black gram Plant Leaf Disease (BPLD) dataset for recognition and classification of diseases using computer-vision algorithms

Abstract

This article introduces Black gram Plant Leaf Disease (BPLD) dataset, which is scientifically called as Vigna Mungo and is popularly known as Urad in India. It is widely considered to be one of the most significant pulse crops farmed in India. Anthracnose, Leaf Crinkle, Powdery Mildew and Yellow Mosaic diseases shown significant impact on the black gram production and causing financial loss to the farmers. A fusion of image processing and computer vison algorithms are widely used in recent years, for applications in the diagnosis and categorization of diseases that affect plant leaves. To detect and classify plant leaf diseases which degrades the quality of the black gram crop, in early stages, using computer vision algorithms, a Black gram Plant Leaf Disease (BPLD) dataset was created and briefly discussed in this article. The dataset holds a total of 1000 images belongs to five classes: four diseases and one healthy. The images in the presented dataset were captured under the real cultivation fields at Nagayalanka, Krishna, Andhra Pradesh, using camera and mobile phones. After the image acquisition, the images were categorized and processed with the help of agriculture experts. Researchers who utilize image processing, machine learning and particularly deep learning algorithms for automated diagnosis and classification of black gram plant leaf diseases in early stage to assist farmers could benefit from this dataset. The dataset is publicly and freely available at https://doi.org/10.17632/zfcv9fmrgv.3