Datasets Comparison

Fruits-360: A dataset of images containing fruits and vegetables Version: 2025.03.11.0 A high-quality, dataset of images containing fruits and vegetables. The following fruits and vegetables are included: Apples (different varieties: Crimson Snow, Golden, Golden-Red, Granny Smith, Pink Lady, Red, Red Delicious), Apricot, Avocado, Avocado ripe, Banana (Yellow, Red, Lady Finger), Beetroot Red, Blueberry, Cactus fruit, Cantaloupe (2 varieties), Carambula, Carrot, Cauliflower, Cherry (different varieties, Rainier), Cherry Wax (Yellow, Red, Black), Chestnut, Clementine, Cocos, Corn (with husk), Cucumber (ripened, various), Dates, Eggplant (normal and long), Fig, Ginger Root, Granadilla, Grape (Blue, Pink, White (different varieties)), Grapefruit (Pink, White), Guava, Hazelnut, Huckleberry, Kiwi, Kaki, Kohlrabi, Kumsquats, Lemon (normal, Meyer), Lime, Lychee, Mandarine, Mango (Green, Red), Mangostan, Maracuja, Melon Piel de Sapo, Mulberry, Nectarine (Regular, Flat), Nut (Forest, Pecan), Onion (Red, White), Orange, Papaya, Passion fruit, Peach (different varieties), Pepino, Pear (different varieties, Abate, Forelle, Kaiser, Monster, Red, Stone, Williams), Pepper (Red, Green, Orange, Yellow), Physalis (normal, with Husk), Pineapple (normal, Mini), Pitahaya Red, Plum (different varieties), Pomegranate, Pomelo Sweetie, Potato (Red, Sweet, White), Quince, Rambutan, Raspberry, Redcurrant, Salak, Strawberry (normal, Wedge), Tamarillo, Tangelo, Tomato (different varieties, Maroon, Cherry Red, Yellow, not ripened, Heart), Walnut, Watermelon, Zucchini (white and dark). Dataset properties Total number of images: 106671. Training set size: 79921 images. Test set size: 26750 images. Number of classes: 160 (fruits, vegetables, nuts and seeds). 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 or r3_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. Repository structure Folders Training and Test contain images for training and testing purposes. Alternate download The Fruits-360 dataset can be downloaded from: https://www.kaggle.com/moltean/fruits https://github.com/fruits-360 How to cite Mihai Oltean, Fruits-360 dataset, 2017-. License: C BY-SA 4.0 Copyright (c) 2017-, Mihai Oltean You are free to: Share and Adapt. Under the following terms: Attribution — You must give appropriate credit and ShareAlike.

Fruits and vegetables 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 I placed a white sheet of paper as background. However due to the variations in the lighting conditions, the background was not uniform and I 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.

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

Creative Commons Attribution 4.0 International

Fruits-360: A dataset of images containing fruits, vegetables, nuts and seeds Version: 2025.03.26.0 The following fruits, vegetables, nuts and seeds are included: Apples (different varieties: Crimson Snow, Golden, Golden-Red, Granny Smith, Pink Lady, Red, Red Delicious), Apricot, Avocado, Avocado ripe, Banana (Yellow, Red, Lady Finger), Beans, Beetroot Red, Blackberry, Blueberry, Cabbage, Caju seed, Cactus fruit, Cantaloupe (2 varieties), Carambula, Carrot, Cauliflower, Cherimoya, Cherry (different varieties, Rainier), Cherry Wax (Yellow, Red, Black), Chestnut, Clementine, Cocos, Corn (with husk), Cucumber (ripened, regular), Dates, Eggplant, Fig, Ginger Root, Goosberry, Granadilla, Grape (Blue, Pink, White (different varieties)), Grapefruit (Pink, White), Guava, Hazelnut, Huckleberry, Kiwi, Kaki, Kohlrabi, Kumsquats, Lemon (normal, Meyer), Lime, Lychee, Mandarine, Mango (Green, Red), Mangostan, Maracuja, Melon Piel de Sapo, Mulberry, Nectarine (Regular, Flat), Nut (Forest, Pecan), Onion (Red, White), Orange, Papaya, Passion fruit, Peach (different varieties), Pepino, Pear (different varieties, Abate, Forelle, Kaiser, Monster, Red, Stone, Williams), Pepper (Red, Green, Orange, Yellow), Physalis (normal, with Husk), Pineapple (normal, Mini), Pistachio, Pitahaya Red, Plum (different varieties), Pomegranate, Pomelo Sweetie, Potato (Red, Sweet, White), Quince, Rambutan, Raspberry, Redcurrant, Salak, Strawberry (normal, Wedge), Tamarillo, Tangelo, Tomato (different varieties, Maroon, Cherry Red, Yellow, not ripened, Heart), Walnut, Watermelon, Zucchini (green and dark). Dataset properties Total number of images: 115499. Training set size: 86554 images. Test set size: 28945 images. Number of classes: 170 (fruits, vegetables, nuts and seeds). 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 or r3_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. Repository structure Folders Training and Test contain images for training and testing purposes. Alternate download The Fruits-360 dataset can be downloaded from: https://www.kaggle.com/moltean/fruits https://github.com/fruits-360 How to cite Mihai Oltean, Fruits-360 dataset, 2017-. License: C BY-SA 4.0 Copyright (c) 2017-, Mihai Oltean You are free to: Share and Adapt. Under the following terms: Attribution — You must give appropriate credit and ShareAlike.

All fruits, vegetables, etc, are from: - my own garden (located in Cugir, Romania) or - purchased, by me, from nearby stores. Fruits and vegetables 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 I placed a white sheet of paper as background. However due to the variations in the lighting conditions, the background was not uniform and I 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.

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

Creative Commons Attribution 4.0 International