BananaVista: Banana Varieties Image Dataset

Published: 29 November 2023| Version 3 | DOI: 10.17632/35gb4v72dr.3


This dataset presents an assortment of high-resolution images that exhibit six well-known banana varieties procured from two distinct regions in Bangladesh. These bananas were thoughtfully selected from rural orchards and local markets, providing a diverse and comprehensive representation. The dataset serves as a visual reference, offering a thorough portrayal of the distinct characteristics of these banana types, which aids in their precise classification. It encompasses six distinct categories, namely, Shagor, Shabri, Champa, Anaji, Deshi, and Bichi, with a total of 1166 original images and 6000 augmented JPG images. These images were diligently captured during the period from August 01 to August 15, 2023. The dataset includes two variations: one with raw images and the other with augmented images. Each variation is further categorized into six separate folders, each dedicated to a specific banana variety. The images are of non-uniform dimensions and have a resolution of 4608 × 3456 pixels. Due to the high resolution, the initial file size amounted to 4.08 GB. Subsequently, data augmentation techniques were applied, as machine vision deep learning models require a substantial number of images for effective training. Augmentation involves transformations like scaling, shifting, shearing, zooming, and random rotation. Specific augmentation parameters included rotations within a range of 1° to 40°, width and height shifts, zoom range, and shear ranges set at 0.2. As a result, an additional 1000 augmented images were generated from the original images in each category, resulting in a dataset comprising a total of 6000 augmented images (1000 per category) with a data size of 4.73 GB.



American International University Bangladesh, Jahangirnagar University, Bangladesh University of Business and Technology


Computer Vision, Image Processing, Machine Learning, Agricultural Development, Pattern Recognition