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, Cauliflower, Cherry (different varieties, Rainier), Cherry Wax (Yellow, Red, Black), Chestnut, Clementine, Cocos, Dates, Eggplant, 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, Williams), Pepper (Red, Green, 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), Walnut.
Total number of images: 82213.
Training set size: 61488 images (one fruit or vegetable per image).
Test set size: 20622 images (one fruit or vegetable per image).
Multi-fruits set size: 103 images (more than one fruit (or fruit class) per image)
Number of classes: 120 (fruits and vegetables).
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.
Folders Training and Test contain images for training and testing purposes.
Folder test-multiple_fruits contains images with multiple fruits. Some of them are partially covered by other fruits. This is an excelent test for real-world detection.
Folder src/image_classification contains the python code for training the neural network. It uses the TensorFlow library.
Folder src/utils contains the C++ code used for extracting the fruits or vegetables from background.
Folder papers contains the research papers related to this dataset.
The dataset can also be downloaded from: Kaggle
Horea Muresan, Mihai Oltean, Fruit recognition from images using deep learning, Acta Univ. Sapientiae, Informatica Vol. 10, Issue 1, pp. 26-42, 2018.
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 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.
Pictures from the test-multiple_fruits folder were made with a Nexus 5X phone.
We have run TensorFlow on these data and the results are presented in this research paper.
Fruits were filmed at the dates given below (YYYY.MM.DD):
2017.02.25 - Apple (golden).
2017.02.28 - Apple (Red Yellow 1, red, golden2), Kiwi, Pear, Grapefruit, Lemon, Orange, Strawberry.
2017.03.05 - Apple (golden3, Braeburn, Granny Smith, red2).
2017.03.07 - Apple (red3).
2017.05.10 - Plum, Peach, Peach flat, Apricot, Nectarine, Pomegranate.
2017.05.27 - Avocado, Papaya, Grape, Cherrie.
2017.12.25 - Carambula, Cactus fruit, Granadilla, Kaki, Kumsquats, Passion fruit, Avocado ripe, Quince.
2017.12.28 - Clementine, Cocos, Mango, Lime, Lychee.
2017.12.31 - Apple Red Delicious, Pear Monster, Grape White.
2018.01.14 - Banana, Grapefruit Pink, Mandarine, Pineapple, Tangelo.
2018.01.19 - Huckleberry, Raspberry.
2018.01.26 - Dates, Maracuja, Plum 2, Salak, Tamarillo.
2018.02.05 - Guava, Grape White 2, Lemon Meyer
2018.02.07 - Banana Red, Pepino, Pitahaya Red.
2018.02.08 - Pear Abate, Pear Williams.
2018.05.22 - Lemon rotated, Pomegranate rotated.
2018.05.24 - Cherry Rainier, Cherry 2, Strawberry Wedge.
2018.05.26 - Cantaloupe (2 varieties).
2018.05.31 - Melon Piel de Sapo.
2018.06.05 - Pineapple Mini, Physalis, Physalis with Husk, Rumbutan.
2018.06.08 - Mulberry, Redcurrant.
2018.06.16 - Cherry Red, Hazelnut, Walnut, Tomato.
2018.06.17 - Cherry Wax (Yellow, Red, Black).
2018.08.19 - Apple Red Yellow 2, Grape Blue, Grape White 2, Grape White 3, Peach 2, Plum 3, Tomato Maroon, Tomato 1-4.
2018.12.20 - Nut Pecan, Pear Kaiser, Tomato Yellow.
2018.12.21 - Banana Lady Finger, Chesnut, Mangostan.
2018.12.22 - Pomelo Sweetie.
2019.04.21 - Apple Crimson Snow, Apple Pink Lady, Blueberry, Kohlrabi, Mango Red, Pear Red, Pepper (Red, Yellow, Green).
2019.06.18 - Beetroot Red, Ginger Root, Nectarine Flat, Nut Forest, Onion Red, Onion Red Peeled, Onion White, Potato Red, Potato Red Washed, Potato Sweet, Potato White.
2019.07.07 - Cauliflower, Eggplant, Pear Forelle.
MIT License
Copyright (c) 2017-2019 Mihai Oltean, Horea Muresan
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