Fruits-360 dataset: A dataset of images containing fruits, vegetables, nuts and seeds

Version: 2025.06.07.0

Content

The following fruits, vegetables and nuts and 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).

Branches

The dataset has 5 major branches:

-The 100x100 branch, where all images have 100x100 pixels. See _fruits-360_100x100_ folder.

-The original-size branch, where all images are at their original (captured) size. See _fruits-360_original-size_ folder.

-The meta branch, which contains additional information about the objects in the Fruits-360 dataset. See _fruits-360_dataset_meta_ folder.

-The multi branch, which contains images with multiple fruits, vegetables, nuts and seeds. These images are not labeled. See _fruits-360_multi_ folder.

-The _3_body_problem_ branch where the Training and Test folders contain different (varieties of) the 3 fruits and vegetables (Apples, Cherries and Tomatoes). See _fruits-360_3-body-problem_ folder.

How to cite

Mihai Oltean, Fruits-360 dataset, 2017-

Dataset properties

For the 100x100 branch

Total number of images: 138704.

Training set size: 103993 images.

Test set size: 34711 images.

Number of classes: 206 (fruits, vegetables, nuts and seeds).

Image size: 100x100 pixels.

For the original-size branch

Total number of images: 58363.

Training set size: 29222 images.

Validation set size: 14614 images

Test set size: 14527 images.

Number of classes: 90 (fruits, vegetables, nuts and seeds).

Image size: various (original, captured, size) pixels.

For the 3-body-problem branch

Total number of images: 47033.

Training set size: 34800 images.

Test set size: 12233 images.

Number of classes: 3 (Apples, Cherries, Tomatoes).

Number of varieties: Apples = 29; Cherries = 12; Tomatoes = 19.

Image size: 100x100 pixels.

For the meta branch

Number of classes: 26 (fruits, vegetables, nuts and seeds).

For the multi branch

Number of images: 150.

Filename format:

For the 100x100 branch

image_index_100.jpg (e.g. 31_100.jpg) or

r_image_index_100.jpg (e.g. r_31_100.jpg) or

r?_image_index_100.jpg (e.g. r2_31_100.jpg)

where "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.

For the original-size branch

r?_image_index.jpg (e.g. r2_31.jpg)

where "r" stands for rotated fruit. "r2" means that the fruit was rotated around the 3rd axis.

The name of the image files in the new version does NOT contain the "_100" suffix anymore. This will help you to make the distinction between the original-size branch and the 100x100 branch.

For the multi branch

The file's name is the concatenation of the names of the fruits inside that picture.

Alternate download

The Fruits-360 dataset can be downloaded from:

Kaggle https://www.kaggle.com/moltean/fruits

GitHub https://github.com/fruits-360

How fruits were filmed

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 a background.

Here i...

Dataset

This dataset was created by ML-50 Mudia Rahmah

Contents

Dataset

This dataset was created by DUDI NURDIYANSAH

Contents

Dataset

This dataset was created by maulida Kiatuddin

Released under Other (specified in description)

Contents

Images were collected from two distinct farms: Heera Recreation & Entertainment in Casarvane VP, Cansarvornem, Goa(Lat 15.704596° and Long 73.877316°) and Mavlangkar’s Cashew Plantation in Fakir patha, Goa(Lat 15.760858° and Long 73.885091°). The dataset comprises images of cashew apples categorized into three maturity stages: ripe, unripe, and overripe. Each maturity group consists of a group of about 300 images totaling to 900 images in the data set from different aspects of cashew apple maturity like the color, texture and shape.

65 percent humidity

Description:

This dataset contains information about various attributes of a set of fruits, providing insights into their characteristics. The dataset includes details such as fruit ID, size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality.

Key Features:

• A_id: Unique identifier for each fruit
• Size: Size of the fruit
• Weight: Weight of the fruit
• Sweetness: Degree of sweetness of the fruit
• Crunchiness: Texture indicating the crunchiness of the fruit
• Juiciness: Level of juiciness of the fruit
• Ripeness: Stage of ripeness of the fruit
• Acidity: Acidity level of the fruit
• Quality: Overall quality of the fruit

Potential Use Cases:

• Fruit Classification: Develop a classification model to categorize fruits based on their features.
• Quality Prediction: Build a model to predict the quality rating of fruits using various attributes.

The dataset was generously provided by an American agriculture company. The data has been scaled and cleaned for ease of use.

If you find this dataset useful, your support through an upvote would be greatly appreciated ❤️🙂 Thank you

Welcome to the Fruit Image Dataseton Kaggle! This dataset contains over 8700 uncleaned images belonging to*** 22 different classes***, consisting of 11 ripe and 11 unripe fruits. This diverse collection of images is a valuable resource for anyone interested in image processing and computer vision tasks, particularly image classification projects.

Whether you're a beginner looking to start your journey in computer vision or an experienced data scientist working on a low-configuration PC, this dataset offers a wide range of possibilities. You can use these images for:

Image Classification: Train machine learning models to accurately classify fruits as ripe or unripe. Object Detection: Build object detection models to identify and locate fruits in images. Image Enhancement: Apply image preprocessing techniques to clean and enhance the dataset for improved model training. Transfer Learning: Leverage pre-trained models to fine-tune and optimize fruit classification tasks. Feel free to download this dataset from my Kaggle account and explore the world of fruit image analysis. Don't forget to share your findings and contributions with the Kaggle community. Happy coding!