![]() The images had the original dimension to be 3096 × 4128 and it was resized again to the dimension of 256 × 256. The images were captured randomly and were sorted with the help of an expert. The images were collected from different Banana plantation fields containing the images of stems, leaves, fruits, and flowers of the plant and some common diseases that affect the plant. The images present in the dataset are collected manually using a good quality mobile phone and a DSLR camera, under bright sunlight, but some of them even fall under the shaded parts of the plant. While the images were captured it was taken into consideration that an average light falls on the images. Raw RGB images of the leaves, stems, and fruits of banana plants were captured under natural light with a mobile phone camera Samsung SM-G610F having 9.6 megapixels and with Nikon SX 70 having 18.3 megapixels. Images of different diseases that affect the banana plants and also the deficiency of the plant. Images of different varieties of banana plants that include the stem, leaf, and fruit images. The purpose of this article is to provide the Researchers and Students in getting access to our dataset that would help them in their research and in developing some machine learning models. A total of 8000+ processed images are present in the dataset. A dataset of Potassium deficiency has been also considered in this article. And the diseases and pathogens that we have considered here are the Bacterial Soft Rot, Banana Fruit Scarring Beetle, Black Sigatoka, Yellow Sigatoka, Panama disease, Banana Aphids, and Pseudo-Stem Weevil. The varieties of Banana plants that we have considered in the dataset are the Malbhog ( Musa assamica), Jahaji ( Musa chinensis), Kachkol ( Musa paradisiaca L.), Bhimkol ( M. This article introduces an image dataset of varieties of banana plants and the diseases related to them. Problem-specific, clean and crisp datasets are also lagging in the sector. Probable processes have been developed worldwide to improve the production of food crops. In recent times, the classification and identification of different fruits and food crops have become a necessity in the field of agricultural science for sustainable growth.
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