This dataset is a customized version of the Fruit Quality Classification dataset originally created by Ryan D. Park (https://www.kaggle.com/datasets/ryandpark/fruit-quality-classification), which provides detailed images of different fruits categorized by quality and type. I have adapted and expanded upon this dataset to create a unique collection focused specifically on detection across various fruit types.

Dataset Overview: - Total Images: 1,968 images - Training Set: 1,852 images - Validation Set: 116 images

Fruit Types:Apple, Banana, Guava, Lime, Orange, Pomegranate Quality Categories: This dataset categorizes fruits into different ripeness stages, capturing a wide range of ripening conditions: - Bad Quality - Good Quality

Use Case: This dataset can be particularly useful for: - Fruit Quality Classification: Aiming to distinguish between quality in fruits, which has applications in agricultural technology, quality control, and the food industry. - AI and Computer Vision Projects: Perfect for training deep learning models to detect ripeness levels from fruit images. - Agricultural Research: Facilitates research into automating the identification of fruit quality and ripeness, a key factor in food logistics and waste reduction.

Dataset Details: Image Dimensions: Images are primarily of size 256x256 pixels, sourced from diverse real-world environments, captured under varying lighting conditions and backgrounds (e.g., top views, front views, rotated orientations). Annotations: Each image is labeled with bounding box coordinates for detecting fruits, facilitating object detection tasks as well as classification of ripeness levels.

Credits: This dataset was developed using images from the publicly available Fruit Quality Classification dataset by Ryan D. Park (https://www.kaggle.com/datasets/ryandpark/fruit-quality-classification). All original credits go to the creator, and I extend my thanks for sharing this dataset. The original dataset contains a much larger collection of images across different fruit types and quality levels, which served as the basis for this specialized ripeness-focused version.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F9519669%2F8c0840417f7b324564f7b0b7819951d4%2FScreenshot%202025-05-02%20194856.png?generation=1746269238707412&alt=media" alt="Example Data view">

The dataset is organized into training and validation sets, with images in various formats and sizes. The annotations are in YOLO format, providing the class ID and bounding box coordinates for each fruit instance. The dataset currently has no explicit preprocessing or augmentation applied. Its intended use is for training and evaluating object detection models for real-time detection, automated recognition, and benchmarking.

Detailed Description in the README.

Kaggle Fruit

## Overview

Kaggle Fruit is a dataset for object detection tasks - it contains Fruit annotations for 289 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 [MIT license](https://creativecommons.org/licenses/MIT).

Dataset

This dataset was created by Shreyas Kulkarni

Released under Apache 2.0

Contents

Produce‑Detect‑Kaggle

## Overview

Produce‑Detect‑Kaggle is a dataset for object detection tasks - it contains Fruits And Vegs 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).

The Nature3 dataset is designed for training and evaluating deep learning models for the detection and classification of plant parts. This dataset includes high-quality images of leaves, flowers, and fruits from various plant species, organized into separate categories. It is formatted specifically for use with the YOLO (You Only Look Once) object detection model, making it ideal for real-time detection tasks.

This dataset contains images in three categories:

Leaf: Images featuring various types of plant leaves.
Flower: Images featuring different types of plant flowers.
Fruit: Images showcasing fruits from different plant species.

Each image is accompanied by its respective YOLO annotation file in the form of .txt files, containing bounding box coordinates and class labels. The dataset is organized into train, val, and test directories, ensuring proper validation and evaluation for machine learning model development.

Dataset Structure:

train: Images used for training the detection models.
val: Images used for model validation during training.
test: Images used to evaluate the model’s performance.

Number of Classes : 3 Class Names : ['***Leaf***', '***Flower***', '***Fruit***']

This dataset is ideal for a variety of applications, including:

• Plant parts recognition using computer vision techniques.
• Agricultural automation to help detect and classify plant parts for automated harvesting or monitoring.
• Research in environmental science and ecology, where identifying plant characteristics is essential.
• Educational use for developing object detection algorithms and models.

With diverse plant species and well-annotated images, the Nature3 dataset provides a robust foundation for developing accurate, real-time object detection models, leveraging YOLO's strengths.

The Olive fruit object detection dataset offers a comprehensive collection of 927 high-resolution images captured manually with a drone over a designated parcel in Jaen, Spain. The dataset serves as a valuable resource for the development and evaluation of object detection algorithms, specifically tailored for detecting olive fruits within an agricultural context.

Each image in the dataset presents unique challenges inherent to the task, including small object sizes, varying lighting conditions, occlusions, and complex backgrounds. The images encompass diverse scenarios encountered in olive orchards, reflecting real-world conditions prevalent in olive farming practices.

The painstaking labeling process ensures precise annotation of olive fruit instances in each image, providing ground truth for training and benchmarking object detection models. Annotation encompasses accurate bounding boxes around individual olive fruits, facilitating the training of algorithms to detect and localize these fruits amidst foliage and other environmental elements. The annotations are located in the labels directory in .txt files associated to each image, containing YOLOv8 format bounding box coordinates.

Researchers, developers, and enthusiasts interested in advancing object detection techniques in agricultural settings will find this dataset invaluable for benchmarking algorithms, developing novel methodologies, and ultimately contributing to enhanced efficiency and productivity in olive cultivation practices.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F6060816%2F6752a54aaae1134d2a2c196bf4c72aa5%2Faceitunas_detected1.jpg?generation=1711034551217538&alt=media" alt="">

Kaggle_fruits_dataset

## Overview

Kaggle_fruits_dataset is a dataset for object detection tasks - it contains Orange annotations for 240 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 [MIT license](https://creativecommons.org/licenses/MIT).

The dataset includes 8479 images of 6 different fruits(Apple, Grapes, Pineapple, Orange, Banana, and Watermelon). Fruits are annotated in YOLOv8 format.

The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 640x640 (Stretch)

The following augmentation was applied to create 3 versions of each source image:

The following transformations were applied to the bounding boxes of each image: * 50% probability of horizontal flip

Dataset

This dataset was created by TanTunJian

Released under CC BY-SA 4.0

Contents

Fruit Classification and Freshness Detection Dataset 🔍 Overview This dataset has been meticulously curated to facilitate research and development in the domain of fruit classification and freshness detection using advanced deep learning techniques. It is designed to support the creation of hybrid models that integrate YOLOv8 for real-time object detection with Convolutional Neural Networks (CNNs) for assessing fruit freshness. The dataset encompasses a diverse range of images captured under varying lighting conditions and angles, simulating real-world scenarios such as grocery stores, farms, and storage facilities.

The dataset comprises 8,099 high-resolution images of three commonly consumed fruits—apples, bananas, and oranges—each categorized into fresh and rotten conditions. Every image has been manually annotated in the YOLO format to aid object detection tasks and labeled for binary classification (Fresh/Rotten), enabling comprehensive model training.

📁 Dataset Structure Total Images: 8,099

Training Set: 6,508 images (80%)

Test Set: 1,591 images (20%)

Classes (6 total):

Fresh Apples

Rotten Apples

Fresh Bananas

Rotten Bananas

Fresh Oranges

Rotten Oranges

Annotations: Provided in YOLO format using LabelImg

Image Format: JPG, resized to 300x300 pixels

Captured With: Smartphone camera under varied lighting and angles

🧠 Applications This dataset is ideal for:

Object Detection using YOLOv8

Freshness classification using CNN

Hybrid models combining detection and classification

Computer vision projects in smart agriculture, food safety, and automated retail systems

📊 Sample Use Case A hybrid deep learning model utilizing this dataset achieved:

Object Detection (YOLOv8):

mAP@0.5: 98%

mAP@0.5:0.95: 87%

Freshness Classification (CNN):

Test Accuracy: 97.6%

These results underscore the dataset’s suitability for high-performance, real-time AI applications in agricultural automation and food quality assessment.

👨‍💻 Contributors Prof. Shubhashree Sahoo

Dr. Sitanath Biswas

Mr. Shubham Kumar Sah

Mr. Chirag Nahata

Special thanks to Dr. Soumobroto Saha and Prof. (Dr.) Partha Sarkar for their invaluable guidance and support throughout this research endeavor.

A fridge item detection dataset made for a university project.

Version Log

v1 ~ Uploaded the images.

v2 ~ Manual edited some labelling errors. ~ Seperated the images into 80% train, 10% val and 10% test. ~ Applied auto orient preprocessing.

Source

Grocery Store Computer Vision Project [Roboflow Universe] Yolo (2024) https://universe.roboflow.com/yolo-9co33/grocery-store-hr9us

food ingredients Dataset [Roboflow Universe] datasets (2023) https://universe.roboflow.com/datasets-cgosu/food-ingredients-7uwno

Fruits-360 dataset [GitHub] Mihai Oltean (2017) https://github.com/fruits-360

Meat Detection Dataset [Roboflow Universe] Meat Detection (2025) https://universe.roboflow.com/meat-detection/meat-detection-8z18t

Shellfish-OpenImages Dataset [Roboflow] Jacob Solawetz (2020) https://public.roboflow.com/object-detection/shellfish-openimages

A Large Scale Fish Dataset [Kaggle] O. Ulucan, D. Karakaya, M. Turkan (2020) https://www.kaggle.com/datasets/crowww/a-large-scale-fish-dataset

About the Dataset

Context

This dataset encompasses images of various fruits and vegetables, providing a diverse collection for image recognition tasks. The included food items are:

• Fruits: Banana, Apple, Pear, Grapes, Orange, Kiwi, Watermelon, Pomegranate, Pineapple, Mango
• Vegetables: Cucumber, Carrot, Capsicum, Onion, Potato, Lemon, Tomato, Radish, Beetroot, Cabbage, Lettuce, Spinach, Soybean, Cauliflower, Bell Pepper, Chilli Pepper, Turnip, Corn, Sweetcorn, Sweet Potato, Paprika, Jalapeño, Ginger, Garlic, Peas, Eggplant

Content

The dataset is organized into three main folders: - Train: Contains 100 images per category. - Test: Contains 10 images per category. - Validation: Contains 10 images per category.

Each of these folders is subdivided into specific folders for each type of fruit and vegetable, containing respective images.

Data Collection

The images in this dataset were sourced using Bing Image Search for a personal project focused on image recognition of food items. The creator does not hold the rights to any of the images included in this dataset. If you are the owner of any image and have concerns regarding its use, please contact the creator to request its removal. The creator will promptly comply with any such requests to ensure all legal obligations are met.

Disclaimer: Users of this dataset are responsible for ensuring that their use of the images complies with applicable copyright laws and regulations. The creator assumes no responsibility for any legal issues that may arise from the use of this dataset. It is recommended to use the dataset for educational and non-commercial purposes only and to seek legal counsel if you have specific concerns about copyright compliance.

Inspiration

The primary motivation behind creating this dataset was to develop an application capable of recognizing food items from photographs. The application aims to suggest various recipes that can be prepared using the identified ingredients.

Citation

Kritik Seth, "Fruits and Vegetables Image Recognition Dataset," Kaggle 2020 [https://www.kaggle.com/kritikseth/fruit-and-vegetable-image-recognition]

Project

This dataset is the data used in this project.

Context

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

Content

3 different fruits:

Apple

Banana

Orange

Acknowledgements

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

Inspiration

I inspired from EdjeElectronics to make my project.

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!

Introducing the most complete publicly available dataset of fruits, vegetables, and berries. I have attempted to classify every fruit and berry that I have discovered in my life. However, vegetables are not fully complete. Fruits and berries were captured with a Europe-Asian bias. Nonetheless, I am still thrilled to share this dataset with the public, as I have carefully curated it over the course of 12 months. As an enthusiast of image classification, I created this dataset as part of my personal project with the dream of building a mobile app that would allow users to log any meal with one-snap calculated calories.

This particular dataset is the second of many that I plan to publish, each featuring various meal items that I have meticulously curated for the most detailed calorie calculation possible.

To create this dataset, I utilized a custom-created mobile app on my iPhone 13-14 Pro Max to capture images. I took great care to ensure that each image was taken from different angles and under varying lighting conditions to provide the most detailed and comprehensive visual representation of each item. I faced challenges with existing apps that were unable to detect meals in my custom neon home lighting, which was one of the reasons why I started working on my own dataset. Additionally, I made a conscious effort to order and select food based on the needs of my dataset, ensuring that each item was thoroughly documented.

In addition to my own pictures, this dataset includes user-generated images sourced from friends, fellow food enthusiasts, and the internet. Each picture was manually classified multiple times by myself and carefully scraped from different sources to ensure accuracy and consistency in the dataset.

I poured my heart and soul into this dataset, making it the project of my life. I had planned to collect pictures for the next 10 years until I started experiencing an eating disorder. As a result, I have decided to make this dataset public in the hopes of sharing my past work for the greater good of the data science community.

Overall, I am proud to share this dataset with public, and I am excited to continue creating and sharing more datasets featuring various meal items in the future.

You can always contact me directly to obtain additional nutrition datasets that I have not yet classified or published. Pictures in my datasets mostly made with the goal of image classification, but also include numerous pictures of various dishes to make object detection possible. I have approximately 20 GB of manually prepared food photos.

Coffee Fruit Maturity ☕🍒

A dataset of coffee (C. Arabica ) cherries at various levels of fruit maturity. Version 1.0 of this dataset is skewed towards green cherries and includes only images of cherries still on the plant, with natural background.

Labels

Number of cherries segmented (whole, partial and background)

Training Split

| Split | | Image count | | --- | --- | -- | |training| 70% | 600| |validation| 20% | 174| |testing| 10% | 85| |**Total images**| |859|

Una base de datos de frutos del cafeto (C. Arabica ) en diferentes niveles de maduración.

Overview This dataset contains a diverse collection of 72,000+ high-quality images of fruits and vegetables, carefully curated for machine learning and deep learning applications. It includes 50 unique categories of fruits and vegetables, such as apples, avocados, carrots, mangoes, broccoli, and more. The dataset is perfect for tasks like classification, object detection, image recognition, and educational purposes.

Key Features Total Images: 72,000+

Image Dimensions: 128x128 pixels (uniform size for consistency and ease of processing). there is also other photos with bigger resloution

Classes: 50 categories of fruits and vegetables, including: Apple, Avocado, Banana, Beetroot, Blackberry, Blueberry, Broccoli, Cabbage, Capsicum, Carrot, Cauliflower, Chilli Pepper, Corn, Cucumber, Dates, Dragonfruit, Eggplant, Fig, Garlic, Ginger, Grapes, Guava, Jalapeno, Kiwi, Lemon, Lettuce, Mango, Mushroom, Okra, Olive, Onion, Orange, Paprika, Peanuts, Pear, Peas, Pineapple, Pomegranate, Potato, Pumpkin, Radish, Rambutan, Soy Beans, Spinach, Strawberry, Sweetcorn, Sweet Potato, Tomato, Turnip, Watermelon.

Split: The dataset is divided into training, validation, and test sets, making it ready for machine learning workflows.

Class Imbalance: Not all categories contain the same number of images, making it suitable for testing class imbalance handling techniques in machine learning.

Why Use This Dataset? Realistic Data Distribution: With varying volumes of data across categories, the dataset provides a realistic challenge for building robust models that can generalize well. Preprocessed and Ready-to-Use: All images are resized to 128x128 pixels, saving you preprocessing time. Diverse Applications: Ideal for fruit and vegetable classification, agriculture-related AI models, health-tech solutions, and educational tools. Large Scale: With over 72,000 images, the dataset is suitable for training deep learning models with high accuracy. Applications Image Classification: Build AI models to classify fruits and vegetables. Health-Tech Solutions: Use the dataset to develop apps for identifying fruits/vegetables for dietary planning. Agricultural Technology: Enhance crop identification systems or supply chain management tools. Education: Provide students and researchers with a practical dataset to learn machine learning techniques. Licensing and Usage This dataset is free to use for any purpose, including research, education, and commercial projects.

Acknowledgments This dataset was created with the goal of advancing AI applications in food technology, agriculture, and education. We hope it helps you build impactful machine learning solutions!

The fruit classification dataset is a collection of images of various fruits used for the purpose of the training and testing computer vision models. The dataset includes five different types of fruit: * Apples * Bananas * Grapes * Mangoes * Strawberries

Each class contains 2000 images, resulting in a total of 10,000 images in the dataset.

The images in the dataset are of various shapes, sizes, and colors, and have been captured under different lighting conditions. The dataset is useful for training and testing models that perform tasks such as object detection, image classification, and segmentation.

The dataset can be used for various research projects, such as developing and testing new image classification algorithms, and for benchmarking existing algorithms. The dataset can also be used to train machine learning models that can be used in real-world applications, such as in the agricultural industry for fruit grading and sorting.

Overall, the fruit classification dataset is a valuable resource for researchers and developers working in the field of computer vision, and its availability will help advance the development of new algorithms and technologies for image analysis and classification.

Data Structure

The data is split into three sets: training, validation, and testing. The training set is used to train the model, while the validation set is used to evaluate the model's performance during training and make adjustments as necessary. The testing set is used to evaluate the final performance of the model after training is complete.

The dataset is split based on a ratio of 97% for training, 2% for validation, and 1% for testing. This means that the training set contains 9700 images (97% of the total), the validation set contains 200 images (2% of the total), and the testing set contains 100 images (1% of the total).

Each class in the dataset is split into three sets based on the ratio. For example, for the "Apple" class, 97% (1940 images) are used for training, 2% (40 images) are used for validation, and 1% (20 images) are used for testing. This ensures that the distribution of classes is consistent across all three sets and that the model is trained on a representative sample of all classes.

Overall, the split of the dataset into training, validation, and testing sets ensures that the model is robust and generalizes well to new, unseen data.

Python Script

The script provided creates train, validation, and test sets from a fruit image dataset by splitting the dataset into predetermined ratios, shuffling the images, and moving them to their respective directories.

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.