Abstract
A dataset of fully labeled images of 20 different kinds of fruits is developed for research purposes in the area of detection, recognition, and classification of fruits. Applications can range from fruit recognition to calorie estimation, and other innovative applications. Using this dataset, researchers are given the opportunity to research and develop automatic systems for the detection and recognition of fruit images using deep learning algorithms, computer vision, and machine learning algorithms. The main contribution is a very large dataset of fully labeled images that are publicly accessible and available for all researchers free of charge. The dataset is called "DeepFruit", which consists of 21,122 fruit images for 8 different fruit set combinations. Each image contains a different combination of four or five fruits. The fruit images were captured on different plate sizes, shapes, and colors with varying angles, brightness levels, and distances. The dataset images were captured with various angles and distances but could be cleared by utilizing the preprocessing techniques that allow for noise removal, centering of the image, and others. Preprocessing was done on the dataset such as image rotation & cropping, scale normalization, and others to make the images uniform. The dataset is randomly partitioned into an 80% training set (16,899 images) and a 20% testing set (4,223 images). The dataset along with the labels is publicly accessible at: https://data.mendeley.com/datasets/5prc54r4rt.