BananaImageBD: An Extensive Image Dataset of Common Bangladeshi Banana Varieties with Different Ripeness Levels

Published: 4 September 2024| Version 1 | DOI: 10.17632/ptfscwtnyz.1
Contributors:
,
,
,
,
,
,
,
,

Description

Type of data: 256x256 px Banana images. Data format: JPEG Contents of the dataset: Banana cultivars and ripeness stages. Number of classes: (1) Four Most Popular Banana cultivars in Bangladesh - Bangla Kola, Chompa Kola, Sabri Kola, and Sagor Kola, and (2) Four Ripeness Stages - Green, Semi-ripe, Ripe, and Overripe Number of images: (1) Total original (raw) images of banana cultivars = 2512, Augmented to 7536 images, and (2) Total original (raw) images of ripeness stages = 825, Augmented to 2460 images. Distribution of instances: (1) Original (raw) images in each class of banana cultivars: Bangla Kola = 444, Champa Kola = 1035, Sabri Kola = 509, and Sagor Kola = 524. (2) Augmented images in each class of banana cultivars: Bangla Kola = 1332, Chompa Kola = 3105, Sabri Kola = 1527, Sagor Kola = 1572. (3) Original (raw) images in each class of Ripeness stages: Green = 213, Semi-ripe = 205, Ripe = 204, and Overripe = 203. (4) Augmented images in each class of Ripeness stages: Green = 639, Semi-ripe = 612, Ripe = 600, and Overripe = 609. Dataset Size: (1) Total size of the original (raw) banana cultivars dataset = 17.5 MB. (2) Total size of the augmented banana cultivars dataset = 80.1 MB. (3) Total size of the original (raw) ripeness stages dataset = 5.58 MB, and (4) Total size of the augmented ripeness stages dataset = 25.4 MB. Data Acuisition Process: Images of bananas are captured using mobile phone cameras. Data Source Location: Local banana wholesale markets and retail fruit shops from different places of Bangladesh. Where applicable: Training machine learning and deep learning models to distinguish popular banana cultivars of Bangladesh and the ripeness stages of bananas.

Files

Institutions

East West University

Categories

Horticulture, Computer Vision, Image Processing, Object Detection, Machine Learning, Food Processing, Image Classification, Banana, Precision Agriculture, Deep Learning

Licence