Mango and Banana Dataset (Ripe Unripe) : Indian RGB image datasets for YOLO object detection

Published: 14 May 2023| Version 3 | DOI: 10.17632/y3649cmgg6.3


'Mango and Banana Dataset (Ripe Unripe)' is the RGB image dataset. This dataset of 5000 photos of bananas and mangoes focuses on identifying ripe and unripe fruits. Each photograph has metadata that identifies whether or not the banana in the image is considered ripe. The data set was gathered in indoor as well as outdoor lighting conditions, to identify ripe and unripe Bananas and Mangoes. Each image in this dataset has a YOLO.txt label attached to it. This data can be used to train all YOLO Object Detection models. The dataset has been divided into two sections: Train and Test each of which contains 80% and 20% of the total data. Train folder contains 4000 images with labels and Test folder contains 1000 images with labels. The purpose of collecting this dataset was to create 'Ripe Unripe Fruit Detection System' using YOLOv8 Object detection model. Dimensions of image : 640 x 480


Steps to reproduce

We have manually collected the above data. Using Lenovo-300 FHD webcam, we continuously collected photos using the OPENcv Python library. On a few photographs from the collection, we used image augmentation to expand the dataset.


Maharashtra Institute of Technology


Computer Vision, Object Detection, Object Recognition, Image Classification, Detection System, Banana, Mango, Deep Learning