Type of Data: 256x256 px Banana images. Data Format: JPEG Contents of the Dataset: Banana varieties and ripeness stages.

Number of Classes: (1) Four most popular banana varieties in Bangladesh - Bangla Kola, Chompa Kola, Sabri Kola, and Sagor Kola, and (2) Four ripeness stages - Green, Semi-ripe, Ripe, and Overripe.

Number of Images: (1) Total Original (Raw) images of banana varieties = 2,471, Augmented to 7,413 images, and (2) Total Original (Raw) images of ripeness stages = 820, Augmented to 2,457 images.

Distribution of Instances: (1) Original (Raw) images in each class of banana varieties: Bangla Kola = 444, Champa Kola = 994, Sabri Kola = 509, and Sagor Kola = 524; (2) Augmented images in each class of banana varieties: Bangla Kola = 1332, Champa Kola = 2,982, Sabri Kola = 1,527, Sagor Kola = 1,572; (3) Original (Raw) images in each class of Ripeness stages: Green = 212, Semi-ripe = 204, Ripe = 201, and Overripe = 203; (4) Augmented images in each class of Ripeness stages: Green = 636, Semi-ripe = 609, Ripe = 603, and Overripe = 609.

Dataset Size: (1) Total size of the Original (Raw) banana varieties dataset = 17.2 MB; (2) Total size of the Augmented banana varieties dataset = 78.5 MB; (3) Total size of the Original (Raw) ripeness stages dataset = 5.55 MB; and (4) Total size of the Augmented ripeness stages dataset = 25.2 MB.

Data Acquisition Process: Images of bananas are captured using high-quality smartphone cameras. Data Source Location: Local banana wholesale markets and retail fruit shops located in different places in Bangladesh. Where Applicable: The dataset presents considerable potential for fostering innovation and developing automated, efficient processes across various industries, such as precision agriculture, food processing, and supply chain management. By training Machine Learning (ML) and Deep Learning (DL) models on this dataset, it becomes possible to accurately classify banana varieties and evaluate their ripeness stages. These models can be utilized to design automated systems for determining ideal harvest times, establishing banana quality control standards, analyzing consumer preferences to guide product development and marketing strategies, and streamlining the supply chain through enhanced harvesting, sorting, packaging, and inventory management. Additionally, researchers focused on advancing Computer Vision technologies in food and agricultural sciences will find the dataset valuable for improving precision farming and food processing methods. As a result, the dataset offers substantial potential for automating banana production and processing, cutting labor costs, and boosting overall operational efficiency.

Note: This dataset is an updated version of "BananaImageBD: An Extensive Image Dataset of Common Bangladeshi Banana Varieties with Different Ripeness Levels", DOI: 10.17632/ptfscwtnyz.1

Labeled Banana images suitable for training and evaluating computer vision and deep learning models.

Dataset Description:

The Banana Ripeness Classification Dataset contains 13,478 images of bananas at various stages of ripeness. This dataset is designed to help in developing models that can classify bananas based on their ripeness, providing insights for agricultural applications.

The dataset is divided into three sets:

Training Set: 11,793 images (87% of the total dataset) used for model training.

Validation Set: 1,123 images (8% of the total dataset) used for model validation and hyperparameter tuning.

Test Set: 562 images (4% of the total dataset) used for final model evaluation to assess generalization performance.

Preprocessing:

Auto-Orient: Applied Resize: Stretch to 416x416 Modify Classes: 2 remapped, 0 dropped

Augmentation:

Outputs per training example: 3 Flip: Horizontal, Vertical 90° Rotate: Clockwise, Counter-Clockwise, Upside Down Crop: 0% Minimum Zoom, 20% Maximum Zoom Rotation: Between -15° and +15° Hue: Between -10° and +10° Saturation: Between -10% and +10% Brightness: Between -10% and +10% Exposure: Between -10% and +10% Blur: Up to 1px

Banana Ripeness Images Datasets

Real Dataset Banana Images The real dataset developed consists of 3,495 images of Cavendish bananas.

Synthetic Dataset Banana Images The synthetic dataset developed consists of 161,280 images of Cavendish bananas.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16850153%2Ff1f7dd7feebca89cefc3f0b9d74fd982%2Fmovies-minions-bananas-wallpaper-preview.jpg?generation=1732031695984694&alt=media" alt=""> This comprehensive banana dataset captures important information about banana samples from different regions and varieties. The key attributes are: ample_id: A unique identifier assigned to each banana sample in the dataset. This allows the samples to be tracked and referenced uniquely.

variety: The cultivar or breed of banana, such as Cavendish, Red Dacca, or Lady Finger. Knowing the specific banana variety provides context about the sample's physical characteristics and growing conditions.

region: The geographic origin of the banana, such as Ecuador, Philippines, or Costa Rica. The region can influence factors like climate, soil, and growing practices that impact the banana's qualities.

quality_score: A numerical score, likely on a scale of 1-4 that rates the overall quality of the banana sample. This could encompass factors like appearance, texture, and lack of defects.

quality_category: A text label that categorizes the quality score into broader groupings like "Excellent" etc This provides an easier-to-understand quality assessment.

ripeness_index: A numerical index representing the ripeness level of the banana, potentially ranging from 1 (green/unripe) to 10 (overripe). This quantifies the maturity of the fruit.

ripeness_category: A text label like "Green", "Yellow", "Ripe", or "Overripe" that corresponds to the ripeness index. This gives a clear, qualitative ripeness classification.

sugar_content_brix: The sugar content of the banana measured in degrees Brix. This is a common way to assess the sweetness and quality of the fruit.

firmness_kgf: The firmness of the banana measured in kilograms-force. This indicates the texture and maturity of the sample.

length_cm: The physical length of the banana in centimeters. This size metric can vary by variety and growing conditions.

Nano-Banana Generated Images

9,457 high-quality images generated using the Nano-Banana model (Google Gemini 2.5 Flash Image Preview).

  Dataset Overview

Total Images: 9,457 images Generation Method: Nano-Banana (Google Gemini 2.5 Flash Image Preview) Storage Format: Optimized binary (Hugging Face Image type) File Organization: Normal large parquet files (not chunked) License: MIT

  Schema

Column Type Description

id int Unique identifier

image Image… See the full description on the dataset page: https://huggingface.co/datasets/bitmind/nano-banana.

Here are a few use cases for this project:

1. Grocery Inventory Management: Retailers could use the "Banana" computer vision model to more effectively manage their fruit inventory. The ability to identify Unripe and Ripe Bananas automatically could help optimize sell-through rates and minimize waste.

2. Agricultural Quality Control: Banana farmers and distributors could use this model to analyze the ripeness of their crops. By identifying unripe and ripe bananas, they can better plan their distribution and minimize potential losses.

3. Health and Nutrition Apps: Developers of food tracking or health apps could implement this model to help users identify their banana's ripeness levels. This could be useful for people needing to control their sugar intake as ripeness influences sugar content.

4. Educational Tools: This model could be integrated into educational software or applications teaching about nutrition, agriculture, or biology. The categorization can help students understand various stages of fruit maturation.

5. Augmented Reality Games and Apps: This model could be used in AR games or learning apps where users interact with everyday objects. For example, it could be used to identify bananas and trigger specific interactions or learning experiences.

1. Bananas are not only nutritious but also delicious. Both raw and ripe bananas are beneficial for health. It is a good source of potassium, vitamin C, vitamin B6, and dietary fiber, according to nutritional guidelines.
2. Potassium in bananas helps regulate blood pressure and supports overall heart health. Its' dietary fiber aids digestion and helps in maintaining gastrointestinal regularity. Natural sugars like glucose, fructose, and sucrose in bananas provide quick and sustained energy.
3. With a low glycemic index, bananas assist in keeping blood sugar levels stable. The fiber content promotes a feeling of fullness, aiding in weight management. It contains serotonin, a neurotransmitter that contributes to a good mood and stress reduction.
4. Vitamin A in bananas supports healthy vision, and they may contribute to age-related macular degeneration prevention. The presence of magnesium and vitamin B6 in bananas helps maintain strong bones. It also contains prebiotic fiber that supports the growth of beneficial gut bacteria, contributing to a healthy digestive system.
5. The prevalence of various diseases associated with bananas highlights the need for proper measures to be taken to mitigate their impact, which may lead to a reduction in banana production on a large scale. Therefore, adopting preventive measures as soon as symptoms of diseases are observed is crucial.
6. Diseases in crops pose a significant challenge to agricultural production, impacting the quality and productivity of the crops. Due to environmental factors, diseases in crops can have adverse effects on both yield and quality. For instance, banana diseases can negatively impact the yield and quality of bananas, leading to significant economic losses for farmers. Traditional methods of identifying and managing crop diseases are often time-consuming, labor-intensive, inefficient, and subjective.
7. Such classification and identification tasks have been a promising area for computer vision in recent years.
8. A large dataset of seven different banana classes—Healthy Leaf, Bract Mosaic Virus Disease, Black Sigatoka, Insect Pest Diseases, Moko Disease, Panama Disease, and Yellow Sigatoka—is shown in order to create machine vision-based algorithms.
9. There are 408 images of bananas in all, taken in actual fields. Then, in order to expand the number of data points, shifting, flipping, zooming, shearing, brightness enhancement, and rotation techniques are used to create a total of 2856 augmented images from these original images.

Dataset merupakan dataset langsung dari sumbernya asli, terdiri dari tingkat kematangan pisang jenis Cavendish dan Ambon. Pisang Cavendish berkontribusi pada tingkat kematangan setengah matang, matang, dan terlalu matang. Pisang Ambon digunakan sebagai pengganti untuk tingkat kematangan mentah dan juga untuk menambahkan warna yang sesuai pada tingkat kematangan terlalu matang. Dataset terdiri dari 491 citra mentah, 606 citra setengah matang, 294 citra matang, dan 474 citra terlalu matang. Semua citra pada dataset memiliki ukuran (resolusi) 224 x 224 piksel. Dataset yang totalnya terdiri dari 1.865 citra dibagi menjadi dua bagian, yaitu 1.490 citra data latih dan 375 citra data test, masing-masing sesuai dengan 80% dan 20% dari total dataset.

Here are a few use cases for this project:

1. Grocery Store Inventory Management: Stores can use the Banana Ripeness Classification model to automatically monitor the ripeness of their banana stock, allowing them to more efficiently manage inventory by replacing overripe and rotten bananas while prioritizing the sale of ripe ones.

2. Produce Quality Control in Supply Chain: Producers and distributors can implement the model to assess the quality and ripeness of bananas during the shipping process, helping to reduce food waste by identifying and addressing ripeness issues before the produce reaches the stores.

3. Automated Crop Harvesting: Farmers can integrate the Banana Ripeness Classification model into robotic harvesting systems, ensuring that only bananas at optimal ripeness stages are picked. This would streamline the harvesting process and potentially lead to higher market value for the produce.

4. Smart Home Kitchen Management: Homeowners can use the model with a smartphone app or smart appliances to monitor the ripeness of bananas and other produce in their kitchen, alerting them to consume or utilize the bananas before they become overripe, promoting healthier eating habits and reducing food waste.

5. Food Industry and Recipe Recommendations: Recipe and meal planning apps can leverage the Banana Ripeness Classification model to suggest tailored recipes based on the user's available banana ripeness level. For example, suggesting banana bread recipes for overripe bananas, or salads and smoothies for ripe ones.

Banana Disease Recognition Dataset Introduction: The Banana Disease Recognition Dataset is a collection of images aimed at facilitating research and development in the field of banana disease detection and classification. This dataset contains a total of 777 images, with 700 images designated for training and 77 images for testing. The dataset encompasses six classes of banana diseases and one class for healthy banana leaves. Each class consists of 100 training images and 11 testing images… See the full description on the dataset page: https://huggingface.co/datasets/as-cle-bert/banana-disease-classification.

Each folder inside the banana image dataset folder contains a banana tier with its six side images. The number corresponds to its sampleID. There are 194 banana tier subjects with six images corresponding to its side views: front, left, right, back, top and bottom. A total of 1,164 banana images.

The banana_features.csv shows the extracted features: RGB (red, green, blue) color values, the image side view, class and finger size. There are four class values with its respective numerical value (1: ‘extra class’, 2: ‘class II’, 3: ‘class I’, 4: ‘reject’) and there are 65, 49, 30 and 50 samples per class, respectively.

Note that the finger size feature was taken by manually measuring the length of the top middle finger of a banana tier ("hand") in millimeter (mm). This specific finger was assumed to have a regular size compared to other fingers.

This dataset can be used for machine learning classification of banana based on its features. In addition, the available images can be used for automating the manual measurement of the top middle finger size.

English Description

This dataset contains a collection of images of Pisang Raja and Pisang Ambon bananas. All images were captured using three different smartphone cameras and taken from two sides of each banana. Each image represents a banana at various ripeness stages, ranging from unripe (green) to overripe (brownish). Images were captured twice daily until the bananas became completely rotten. The filename format follows: {phone_brand}_{image_initial}_H{number_of_days}F{capture_phase}{left/right}, indicating the device used, image identity, recording day, capture phase, and side of the fruit.

Varieties: Pisang Raja, Pisang Ambon Devices: Three smartphone brands with different camera specifications Image Type: RGB Applications: Ripeness estimation, fruit quality analysis, and computer vision model development

Indonesian Description

Dataset ini berisi kumpulan citra Pisang Raja dan Pisang Ambon. Seluruh citra diambil menggunakan tiga kamera ponsel dengan merek yang berbeda dan diambil dari dua sisi pisang. Setiap citra pisang berada pada berbagai tingkat kematangan, mulai dari mentah (berwarna hijau) hingga sangat matang (berwarna kecokelatan). setiap citra diambil selama dua kali sehari sampai pisang benar benar busuk dengan format nama file : {merek hp}_{inisial gambar}_H{jumlah hari}F{fase pengambilan gambar}_kiri/kanan {posisi pengambilan gambar.

Varietas: Pisang Raja, Pisang Ambon Perangkat: Tiga merek ponsel dengan spesifikasi kamera berbeda Jenis Citra: RGB Kegunaan: Estimasi tingkat kematangan, analisis kualitas buah, dan pengembangan model penglihatan komputer

Banana Banana

## Overview

Banana Banana is a dataset for object detection tasks - it contains Banana annotations for 1,467 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).

🍌 Banalyzer - Banana Ripeness Classification Dataset A deep learning image dataset for classifying bananas into 4 ripeness stages: Unripe, Ripe, Overripe, and Rotten. Built using transfer learning with MobileNetV2 for efficient training and deployment.

📦 What's Included Image Dataset: Organized training and test sets for all 4 ripeness classes Training Script (train.py): MobileNetV2 transfer learning implementation with data augmentation Prediction Script (predict.py): Command-line tool for single image classification Web Interface (streamlitapp.py): Interactive Streamlit app with camera support Complete Documentation: README with setup and usage instructions

🎯 Use Cases Food quality control and automated sorting Reducing food waste through optimal timing Learning computer vision and transfer learning Building production-ready classification systems

Made with ❤️ using TensorFlow & Streamlit ⭐ If you find this dataset useful, please upvote and share your results! 📖 Full documentation in README.md | 🐛 Report issues in discussions | 💡 Share your projects!

AtharavJadhav/Banana dataset hosted on Hugging Face and contributed by the HF Datasets community

The dataset is a collection of images representing various conditions of bananas, specifically aimed at training machine learning models for image classification or augmentation tasks. The dataset is organized into multiple subfolders, each representing a different condition or class of bananas. These classes include:

Healthy Bananas Bananas with Fusarium Wilt Bananas with Natural Leaf Death Bananas with Rhizome Root Issues Each image in the dataset is initially stored in its respective class folder and typically contains a banana or bananas under different conditions, viewed from different angles, and possibly with varying levels of resolution or lighting.

The dataset is then processed for various machine learning tasks like classification, detection, or augmentation. Specifically, this dataset is aimed at providing a variety of augmented images to ensure a more robust training set, which is critical for improving the generalization performance of machine learning models.

Related : Shuvo, Shuvo Kumar Basak (2025), “Banana_Tree_Disease_Detection_Dataset(BTDDD)”, Mendeley Data, V2, doi: 10.17632/vp2xnb8zmb.2

I, Shuvo Kumar Basak, have created and curated the Dataset. This dataset is freely available for research, educational, and non-commercial purposes.

Free Access to the Dataset: This is available free of charge to all individuals and organizations for educational and research use. This is to support the advancement of knowledge and studies related to biodiversity, machine learning, and related fields.

Future Collaboration and Data Requests: While the dataset is provided free of charge, I encourage individuals and organizations to contact me directly if they need access to additional related data, further assistance, or if they plan on expanding their research in the future.

If you require any new data or specific related datasets, feel free to reach out to me, Shuvo Kumar Basak, for collaboration. I am happy to assist with additional data collection, cleaning, resizing, or other related services at a reasonable cost.

Paid Services - Hire for Data Collection: If you or your organization need custom data collection or wish to obtain related datasets beyond what is included in this collection, I offer a paid service to gather new data according to your specific requirements. This includes: Custom data collection for other tree species or related botanical data.

Data cleaning, resizing, and preprocessing to make the data ready for analysis.

Please contact me for a custom quote based on your specific needs. I will work with you to provide high-quality, tailored datasets to support your research, project, or business needs. Terms and Conditions: The dataset is intended for academic, research, and non-commercial purposes only. Redistribution or commercial use of the dataset without prior written consent is not permitted. Proper attribution to Shuvo Kumar Basak as the creator of the dataset should be provided when using the dataset in publications, projects, or other works.

**More Dataset:: ** 1. https://www.kaggle.com/shuvokumarbasak4004/datasets 2. https://www.kaggle.com/shuvokumarbasak2030 …………………………………..Note for Researchers Using the dataset………………………………………………………………………

This dataset was created by Shuvo Kumar Basak. If you use this dataset for your research or academic purposes, please ensure to cite this dataset appropriately. If you have published your research using this dataset, please share a link to your paper. Good Luck.

Banana Ripening Time-Series Dataset

Overview

This dataset contains time-series image data tracking the ripening process of bananas over an 8-day period. It is designed for machine learning tasks related to freshness detection, ripening stage classification, and shelf-life prediction.

Dataset Structure

The dataset consists of 96 observations organized in a CSV file with the following structure:

Columns

• banana_id (1-12): Unique identifier for each individual banana specimen
• day_number (1-8): The day of observation in the ripening timeline
• days_remaining (7-0): Number of days remaining until the banana reaches its final ripening stage
• split (train/test): Dataset partition for machine learning (75% train, 25% test)
• file_path: Relative path to the corresponding image file

Key Statistics

• Total observations: 96 entries
• Number of unique bananas: 12 specimens
• Observation period: 8 days (Day 1 to Day 8)
• Training samples: 72 images (Bananas 1-9)
• Test samples: 24 images (Bananas 10-12)
• Images per banana: 8 time-series observations

Dataset Characteristics

Time-Series Nature

Each banana is photographed once per day for 8 consecutive days, creating a complete ripening timeline from fresh (Day 1, 7 days remaining) to fully ripe (Day 8, 0 days remaining).

Train-Test Split

The dataset uses a specimen-based split rather than a random split: - Training set: Bananas 1-9 (all their time points) - Test set: Bananas 10-12 (all their time points)

This approach ensures that the model is tested on completely unseen banana specimens, providing a more realistic evaluation of generalization capability.

Potential Use Cases

1. Ripening Stage Classification: Classify bananas into different ripeness categories based on visual appearance
2. Freshness Prediction: Predict the number of days remaining before a banana reaches a specific ripeness level
3. Quality Control: Automated sorting and grading in agricultural or retail settings
4. Time-Series Analysis: Study the progression of visual changes during the ripening process
5. Computer Vision: Train models for food quality assessment and produce monitoring

Data Format

The CSV file follows a standard format with comma-separated values. Image paths reference JPEG files stored in the "Banana Images Dataset" directory with the naming convention: Banana{ID},{DAY}.jpeg

Notes

• Images are expected to be in JPEG format
• The days_remaining field provides a regression target for predictive modeling
• The consistent time-series structure allows for sequential modeling approaches (e.g., RNN, LSTM)
• The specimen-based train-test split is important to maintain when training models

Recommended Preprocessing

• Normalize image dimensions if they vary
• Apply data augmentation carefully to preserve temporal consistency
• Consider using the time-series structure for sequential modeling
• Ensure the train-test split is respected to avoid data leakage

Citation

If you use this dataset, please provide appropriate attribution to the original data source.

Dataset Version: 1.0
Last Updated: 2026

This description is AI generated

This dataset is developed to facilitate research on banana ripening and spoilage prediction using image analysis. By capturing the natural progression of bananas from fresh to rotten over multiple days and varieties, it provides a valuable resource for developing machine learning models that can assist in food quality assessment, agricultural monitoring, and supply chain management. The dataset contributes to the broader goal of reducing food waste and improving post-harvest management by enabling accurate prediction or classification of banana ripeness stages.

Its diverse representation of six banana families and varied imaging conditions supports robust model training for real-world scenarios. The dataset’s versatility allows it to be used for both regression tasks predicting days to rot and classification tasks categorizing ripeness levels, making it relevant for researchers and practitioners in computer vision, agriculture, and food technology.