Fruits 360 dataset

Published: 20 October 2018| Version 1 | DOI: 10.17632/rp73yg93n8.1
Mihai Oltean


Fruits 360 dataset: A dataset of images containing fruits Version: 2018.09.07.0 The following fruits are included: Apples (different varieties: Golden, Golden-Red, Granny Smith, Red, Red Delicious), Apricot, Avocado, Avocado ripe, Banana (Yellow, Red), Cactus fruit, Cantaloupe (2 varieties), Carambula, Cherry (different varieties, Rainier), Cherry Wax (Yellow, Red, Black), Clementine, Cocos, Dates, Granadilla, Grape (Pink, White, White2), Grapefruit (Pink, White), Guava, Huckleberry, Kiwi, Kaki, Kumsquats, Lemon (normal, Meyer), Lime, Lychee, Mandarine, Mango, Maracuja, Melon Piel de Sapo, Mulberry, Nectarine, Orange, Papaya, Passion fruit, Peach, Pepino, Pear (different varieties, Abate, Monster, Williams), Physalis (normal, with Husk), Pineapple (normal, Mini), Pitahaya Red, Plum, Pomegranate, Quince, Rambutan, Raspberry, Salak, Strawberry (normal, Wedge), Tamarillo, Tangelo, Tomato (different varieties, Maroon, Cherry Red), Walnut. Dataset properties Total number of images: 55244. Training set size: 41322 images (one fruit per image). Test set size: 13877 images (one fruit per image). Multi-fruits set size: 45 images (more than one fruit (or fruit class) per image) Number of classes: 81 (fruits). Image size: 100x100 pixels. Filename format: image_index_100.jpg (e.g. 32_100.jpg) or r_image_index_100.jpg (e.g. r_32_100.jpg) or r2_image_index_100.jpg. "r" stands for rotated fruit. "r2" means that the fruit was rotated around the 3rd axis. "100" comes from image size (100x100 pixels). Different varieties of the same fruit (apple for instance) are stored as belonging to different classes. How we made it Fruits were planted in the shaft of a low speed motor (3 rpm) and a short movie of 20 seconds was recorded. A Logitech C920 camera was used for filming the fruits. This is one of the best webcams available. Behind the fruits we placed a white sheet of paper as background. However due to the variations in the lighting conditions, the background was not uniform and we wrote a dedicated algorithm which extract the fruit from the background. This algorithm is of flood fill type: we start from each edge of the image and we mark all pixels there, then we mark all pixels found in the neighborhood of the already marked pixels for which the distance between colors is less than a prescribed value. We repeat the previous step until no more pixels can be marked. All marked pixels are considered as being background (which is then filled with white) and the rest of pixels are considered as belonging to the object. The maximum value for the distance between 2 neighbor pixels is a parameter of the algorithm and is set (by trial and error) for each movie. Published research papers Horea Muresan, Mihai Oltean, Fruit recognition from images using deep learning, Acta Univ. Sapientiae, Informatica Vol. 10, Issue 1, pp. 26-42, 2018. License MIT License Copyright (c) 2017-2018 Mihai Oltean, Horea Muresan



Universitatea 1 Decembrie 1918 din Alba Iulia


Image Processing, Data Analysis, Fresh Fruits, Convolutional Neural Network