Fruits Apple Banana

## Overview

Fruits Apple Banana is a dataset for object detection tasks - it contains Fruits annotations for 1,000 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

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...

Apple Banana Orange Detection

## Overview

Apple Banana Orange Detection is a dataset for object detection tasks - it contains Apple Banana Orange annotations for 400 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset, initially consisting of 10,154 high-resolution images of five fruit types—apple, banana, mango, orange, and grapes—has been expanded to over 81,000 using advanced augmentation techniques like rotation, scaling, and brightness adjustment. The images are classified into three key categories: fresh, rotten, and formalin-mixed. This dataset offers a unique opportunity for researchers in computer vision, agriculture, and food safety to develop machine learning and deep learning models for tasks such as real-time fruit quality assessment, contamination detection, and automating food inspection processes. Its extensive and diverse collection makes it a valuable resource for innovations in public health, export quality control, and agricultural productivity.

(1) Everyone seeks fresh and high-quality fruits, as fruits naturally degrade over time, leading to spoilage. It is estimated that roughly one-third of harvested fruits rot, causing significant financial loss. Additionally, the sale of fruits is impacted by consumer concerns over the health risks associated with consuming spoiled or chemically treated fruits. The manual classification of fresh and rotten fruits by individuals is inefficient, particularly for farmers, sellers, and the fruit processing industry. (2) In recent years, computer vision techniques have shown great promise in automating tasks like classification and detection of fresh and rotten fruits. (3) To aid in the development of computer vision-based algorithms, we present an extensive fruit dataset containing five fruit classes: Fresh Apple, Rotten Apple, Formalin-mixed Apple, Fresh Banana, Rotten Banana, Formalin-mixed Banana, Fresh Grape, Rotten Grape, Formalin-mixed Grape, Fresh Mango, Rotten Mango, Formalin-mixed Mango, Fresh Orange, Rotten Orange, and Formalin-mixed Orange. Classification into fresh, rotten, and formalin-treated categories was carried out with the assistance of agricultural experts.

Fruits Apple Banana 2

## Overview

Fruits Apple Banana 2 is a dataset for object detection tasks - it contains Fruits annotations for 999 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Context

This dataset contains images of 6 fruits and vegetables: apple, banana, bitter gourd, capsicum, orange, and tomato. The images of each fruit or vegetable are grouped into two categories: fresh and stale. The purpose behind the creation of this dataset is the development of a machine learning model to classify fruits and vegetables as fresh or stale. This feature is a part of our final year project titled ‘Food Aayush’. (Github Link)

Data Collection and Preprocessing

For collecting the images to create the dataset, images of the fruits and vegetables were captured daily using a mobile phone camera. Depending on the visual properties of the fruit or vegetable in each image and on the day when the image was captured, each image was labelled as fresh or stale. Additionally, videos of the fruits and vegetables were taken, and the frames of these videos were extracted to collect a large number of images conveniently. The machine learning model requires a 224 x 224-pixel image. So, the images were cropped to extract the center square of the image and resized in 512 x 512 pixels using a data pre-processing library in Keras. Frame Extraction

Data Augmentation: We used ImageDataGenerator library from Keras for augmentation. We on average created 20 augmentations per image which indeed improve our models accuracy. Data Augmentation

Acknowledgements

We would like to give credit to this dataset as we have obtained the images in some of the classes from here. Dataset

Inspiration

Our BE final year project, titled ‘Food Aayush’, is an application that can be used for the classification of fruits and vegetables as fresh or stale, the classification of cooking oils into different rancidity levels, and the analysis of various parameters related to the nutritional value of food and people’s dietary intake. We have trained a machine learning model for the classification of fruits and vegetables. This dataset was created for training the machine learning model. Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Apple Banana Mango

## Overview

Apple Banana Mango is a dataset for object detection tasks - it contains Apple Banana Mango annotations for 300 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Apple+banana+guava+orange Freshness Detection

## Overview

Apple+banana+guava+orange Freshness Detection is a dataset for object detection tasks - it contains Fresh Orange Spoiled Orange DXaO Fresh Apple Spoiled Apple Fruits Fresh Orange Spoiled Orange EbRk annotations for 398 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Fruits-262 dataset: A dataset containing a vast majority of the popular and known fruits

Versions and Updates:

The dataset's final version is the one presented here. No expectations for further development of the dataset at this moment.

If there seem to be any problems with the dataset, its descriptions, its properties or anything at all please contact me and I will help in what ways I can.

15/12/2021 - SYNASC2021 Paper

Based on the Bachelor's Thesis, a paper has been built and published at the SYNASC 2021 conference.

25/05/2021 - Bachelor's Thesis

The thesis, that had at its core building this fruit dataset and training CNN models on it, is finished and has been added. Information about the tasks and phases through which the dataset has been can also be found within the thesis.

25/05/2021 - Resized Dataset and Useful Scripts.

The resized dataset has been uploaded on 5 different dimensions along with some scripts that help in organizing and altering the dataset. The deployment took around 3-4 hours since some errors kept appearing when uploading. Sorry for any inconvenience.

Content

The following fruit types/labels/clades are included: abiu, acai, acerola, ackee, alligator apple, ambarella, apple, apricot, araza, avocado, bael, banana, barbadine, barberry, bayberry, beach plum, bearberry, bell pepper, betel nut, bignay, bilimbi, bitter gourd, black berry, black cherry, black currant, black mullberry, black sapote, blueberry, bolwarra, bottle gourd, brazil nut, bread fruit, buddha s hand, buffaloberry, burdekin plum, burmese grape, caimito, camu camu, canistel, cantaloupe, cape gooseberry, carambola, cardon, cashew, cedar bay cherry, cempedak, ceylon gooseberry, che, chenet, cherimoya, cherry, chico, chokeberry, clementine, cloudberry, cluster fig, cocoa bean, coconut, coffee, common buckthorn, corn kernel, cornelian cherry, crab apple, cranberry, crowberry, cupuacu, custard apple, damson, date, desert fig, desert lime, dewberry, dragonfruit, durian, eggplant, elderberry, elephant apple, emblic, entawak, etrog, feijoa, fibrous satinash, fig, finger lime, galia melon, gandaria, genipap, goji, gooseberry, goumi, grape, grapefruit, greengage, grenadilla, guanabana, guarana, guava, guavaberry, hackberry, hard kiwi, hawthorn, hog plum, honeyberry, honeysuckle, horned melon, illawarra plum, indian almond, indian strawberry, ita palm, jaboticaba, jackfruit, jalapeno, jamaica cherry, jambul, japanese raisin, jasmine, jatoba, jocote, jostaberry, jujube, juniper berry, kaffir lime, kahikatea, kakadu plum, keppel, kiwi, kumquat, kundong, kutjera, lablab, langsat, lapsi, lemon, lemon aspen, leucaena, lillipilli, lime, lingonberry, loganberry, longan, loquat, lucuma, lulo, lychee, mabolo, macadamia, malay apple, mamey apple, mandarine, mango, mangosteen, manila tamarind, marang, mayhaw, maypop, medlar, melinjo, melon pear, midyim, miracle fruit, mock strawberry, monkfruit, monstera deliciosa, morinda, mountain papaya, mountain soursop, mundu, muskmelon, myrtle, nance, nannyberry, naranjilla, native cherry, native gooseberry, nectarine, neem, nungu, nutmeg, oil palm, old world sycomore, olive, orange, oregon grape, otaheite apple, papaya, passion fruit, pawpaw, pea, peanut, pear, pequi, persimmon, pigeon plum, pigface, pili nut, pineapple, pineberry, pitomba, plumcot, podocarpus, pomegranate, pomelo, prikly pear, pulasan, pumpkin, pupunha, purple apple berry, quandong, quince, rambutan, rangpur, raspberry, red mulberry, redcurrant, riberry, ridged gourd, rimu, rose hip, rose myrtle, rose-leaf bramble, saguaro, salak, salal, salmonberry, sandpaper fig, santol, sapodilla, saskatoon, sea buckthorn, sea grape, snowberry, soncoya, strawberry, strawberry guava, sugar apple, surinam cherry, sycamore fig, tamarillo, tangelo, tanjong, taxus baccata, tayberry, texas persimmon, thimbleberry, tomato, toyon, ugli fruit, vanilla, velvet tamarind, watermelon, wax gourd, white aspen, white currant, white mulberry, white sapote, wineberry, wongi, yali pear, yellow plum, yuzu, zigzag vine, zucchini

Dataset properties

Total number of images: 225,640.

Number of classes: 262 fruits.

Number of images per label: Average: 861, Median: 1007, StDev: 276. (Initial target was 1,000 per label)

Image Width: Average: 213, Median: 209, StDev: 19.

Image Height: Average: 262, Median: 255, StDev: 30.

Missing Images from the initial 1,000 target: Average: 580, Median: 567, StDev: 258.

Format: a directory name represents a label and in each directory all the image data under the said label (the images are numbered but there might be missing numbers. The "renumber.py" script, if run, will fix the number gap problem).

Different varieties of the same fruit are generally stored in the same directory (Example: green, yellow and red apple).

The fruit images present in the dataset can contain the fruit in all the stages o...

Apple+banana+guava Freshness Detection

## Overview

Apple+banana+guava Freshness Detection is a dataset for object detection tasks - it contains Fresh Orange Spoiled Orange DXaO Fresh Apple Spoiled Apple Fruits annotations for 398 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

1) Data Introduction • The Fruit classification(10 Class) Dataset is a computer vision dataset designed to classify 10 types of fruits. It consists of 10 classes: apple, banana, kiwi, cherry, orange, mango, avocado, pineapple, strawberry, and watermelon.

2) Data Utilization (1) Characteristics of the Fruit classification(10 Class) Dataset: • The dataset includes images taken under various lighting conditions, backgrounds, and angles, making it suitable for generalization in real-world applications.

(2) Applications of the Fruit classification(10 Class) Dataset: • Development of fruit image classification models: The dataset can be used to train AI models that automatically classify images into 10 different fruit categories. • Applied research in agriculture and distribution sectors: It can be used to develop prototype systems for fruit classification automation, quality inspection support, and smart farming services.

1) Data Introduction • The Fruit Classification Dataset is designed to classify different types of fruits based on their spatial coordinates. It includes data points with 'x' and 'y' coordinates and their corresponding fruit class labels (apple, banana, orange), facilitating the development and testing of classification models for simple geometric data.

2) Data Utilization (1) Fruit Classification data has characteristics that: • It contains detailed coordinates (x and y) for each fruit class, allowing for the visualization and analysis of fruit distribution in a two-dimensional space. This dataset is ideal for understanding basic classification algorithms and testing their performance. (2) Fruit Classification data can be used to: • Machine Learning Education: Supports the teaching and learning of classification techniques, data visualization, and feature extraction in an accessible and engaging manner. • Algorithm Testing: Provides a straightforward dataset for evaluating and comparing the performance of various classification algorithms in distinguishing between different fruit types based on coordinates.

This dataset, initially consisting of 10,154 high-resolution images of five fruit types—apple, banana, mango, orange, and grapes—has been expanded to over 81,000 using advanced augmentation techniques like rotation, scaling, and brightness adjustment. The images are classified into three key categories: fresh, rotten, and formalin-mixed. This dataset offers a unique opportunity for researchers in computer vision, agriculture, and food safety to develop machine learning and deep learning models for tasks such as real-time fruit quality assessment, contamination detection, and automating food inspection processes. Its extensive and diverse collection makes it a valuable resource for innovations in public health, export quality control, and agricultural productivity.

(1) Everyone seeks fresh and high-quality fruits, as fruits naturally degrade over time, leading to spoilage. It is estimated that roughly one-third of harvested fruits rot, causing significant financial loss. Additionally, the sale of fruits is impacted by consumer concerns over the health risks associated with consuming spoiled or chemically treated fruits. The manual classification of fresh and rotten fruits by individuals is inefficient, particularly for farmers, sellers, and the fruit processing industry. (2) In recent years, computer vision techniques have shown great promise in automating tasks like classification and detection of fresh and rotten fruits. (3) To aid in the development of computer vision-based algorithms, we present an extensive fruit dataset containing five fruit classes: Fresh Apple, Rotten Apple, Formalin-mixed Apple, Fresh Banana, Rotten Banana, Formalin-mixed Banana, Fresh Grape, Rotten Grape, Formalin-mixed Grape, Fresh Mango, Rotten Mango, Formalin-mixed Mango, Fresh Orange, Rotten Orange, and Formalin-mixed Orange. Classification into fresh, rotten, and formalin-treated categories was carried out with the assistance of agricultural experts.

Apple Banana Detection

## Overview

Apple Banana Detection is a dataset for object detection tasks - it contains Objects annotations for 3,664 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Fruit Snacks Market was valued at $15.07 B in 2023, and is projected to reach $USD 29.88 B by 2032, at a CAGR of 8.02% from 2023 to 2032.

ObjectiveExtensive research has consistently shown the beneficial impact of fruit consumption on overall health. While some studies have proposed a potential association between fruit consumption and hypertension management, the influence of fruit consumption on mortality rates among hypertensive individuals remains uncertain. Consequently, aim of this study is to evaluate whether fruit consumption is associated with all-cause mortality among hypertensive patients.MethodsData were obtained from the National Health and Nutrition Examination Survey (NHANES), conducted between 2003 and 2006. Ten-year follow-up data from the National Death Index (NDI) were used to assess all-cause mortality. Cox proportional hazard model was utilized to explore the impact of fruit intake on all-cause mortality among hypertensive individuals.ResultsThe study included a cohort of 2,480 patients diagnosed with hypertension, and during the follow-up period, a total of 658 deaths from various causes were recorded. The COX regression analysis demonstrated that hypertensive patients who consumed apples three to six times per week exhibited a significantly reduced risk of all-cause mortality (HR = 0.60, 95%CI: 0.45–0.78, p 

The dataset contains year-wise historically compiled data on the total area sown and quantity of fruits produced in India. The different types of fruits covered in the dataset include almond, aonla/gooseberry, apple, bael, banana, ber, custard apple, grapes, guava, jack fruit, kiwi, lime/lemon, litchi, mandarin (M. Orange, Kinnow, Orange), mango, muskmelon, papaya, passion fruit, peach, pear, picanut, pineapple, plum, pomegranate. sapota, strawberry, sweet orange (malta, mosambi), walnut, watermelon, citrus, grape, etc.

Context

A different dataset for object detection. 240 images in train folder. 60 images in test folder.

This Dataset used in the blog post: link.url

As demonstration for AutoGluon Object Detection Task

Content

3 different fruits:

Apple

Banana

Orange

Acknowledgements

.xml files were created with LabelImg. It is super easy to label objects in images.

Apple Citrus Banana Kiwi

## Overview

Apple Citrus Banana Kiwi is a dataset for object detection tasks - it contains Fruits annotations for 988 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Measurement data for monitoring radioactivity in the environment, in food and feed