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

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.

Banana cultivation is frequently challenged by various diseases that severely impact yield. These diseases detrimentally affect banana plants, causing growth inhibition, diminished fruit production, and even plant fatality. The consequences are far-reaching, as afflicted plants struggle to yield marketable fruit, leading to financial setbacks for banana growers and the potential to disrupt the global banana supply.

Banana Leaf Spot Diseases in the Dataset

The dataset comprises a diverse collection of images showcasing three prominent banana leaf spot diseases, namely: 1. Sigatoka 2. Cordana 3. Pestalotiopsis Additionally, images depicting healthy banana leaves are incorporated for comprehensive analysis.

How the Images Were Captured?

The images were captured using smartphone cameras in the banana fields of Bangabandhu Sheikh Mujibur Rahman Agricultural University, Bangladesh, and nearby banana fields in June 2021. All images were labelled by an expert plant pathologist.

Dataset Description and Organization

The dataset is constituted of two subsets. a) Original Set: This comprises 937 RGB images, divided into 4 classes and provided in JPG format. b) Augmented Set: This set supplements the original collection with 400 images per class, culminating in a total of 1600 images. Employing augmentation techniques, such as Gaussian blur, horizontal flip, cropping, linear contrast adjustment, shear, translation, and rotational shear, we enhanced the dataset's diversity. All images have a standard resolution of 224 x 224 pixels.

Publications Related to this Dataset

Please consider reading the following research articles based on this dataset: 1. BananaSqueezeNet: A very fast, lightweight convolutional neural network for the diagnosis of three prominent banana leaf diseases 2. Bananalsd: A Banana Leaf Images Dataset for Classification of Banana Leaf Diseases Using Machine Learning

Citation

If you're using this dataset for your work, please cite the following articles: @article{bhuiyan2023bananasqueezenet, title={BananaSqueezeNet: A very fast, lightweight convolutional neural network for the diagnosis of three prominent banana leaf diseases}, author={Bhuiyan, Md Abdullahil Baki and Abdullah, Hasan Muhammad and Arman, Shifat E and Rahman, Sayed Saminur and Al Mahmud, Kaies}, journal={Smart Agricultural Technology}, volume={4}, pages={100214}, year={2023}, publisher={Elsevier} } @article{arman2023bananalsd, title={BananaLSD: A banana leaf images dataset for classification of banana leaf diseases using machine learning}, author={Arman, Shifat E and Bhuiyan, Md Abdullahil Baki and Abdullah, Hasan Muhammad and Islam, Shariful and Chowdhury, Tahsin Tanha and Hossain, Md Arban}, journal={Data in Brief}, pages={109608}, year={2023}, publisher={Elsevier} }

Banana Dataset

## Overview

Banana Dataset is a dataset for object detection tasks - it contains Fruits annotations for 550 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.

Raw Fresh Rotten Banana

## Overview

Raw Fresh Rotten Banana is a dataset for classification tasks - it contains Banana annotations for 249 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 Standard Classification (Banana Dataset) dataset consists of two numerical variables and one categorical variable.

2) Data Utilization (1) Standard Classification (Banana Dataset) data has characteristics that: • It consists of three columns and has 5300 pieces. (2) Standard Classification (Banana Dataset) data can be used to: • Model Study: It helps to learn different classification algorithms such as logistic regression, decision tree, random forest, support vector machine, etc.

Banana Disease Recognition

## Overview

Banana Disease Recognition is a dataset for classification tasks - it contains Banana Leaf Diseases annotations for 408 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).

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.

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.

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.

This dataset presents an assortment of high-resolution images that exhibit six well-known banana varieties procured from two distinct regions in Bangladesh. These bananas were thoughtfully selected from rural orchards and local markets, providing a diverse and comprehensive representation. The dataset serves as a visual reference, offering a thorough portrayal of the distinct characteristics of these banana types, which aids in their precise classification. It encompasses six distinct categories, namely, Shagor, Shabri, Champa, Anaji, Deshi, and Bichi, with a total of 1166 original images and 6000 augmented JPG images. These images were diligently captured during the period from August 01 to August 15, 2023. The dataset includes two variations: one with raw images and the other with augmented images. Each variation is further categorized into six separate folders, each dedicated to a specific banana variety. The images are of non-uniform dimensions and have a resolution of 4608 × 3456 pixels. Due to the high resolution, the initial file size amounted to 4.08 GB. Subsequently, data augmentation techniques were applied, as machine vision deep learning models require a substantial number of images for effective training. Augmentation involves transformations like scaling, shifting, shearing, zooming, and random rotation. Specific augmentation parameters included rotations within a range of 1° to 40°, width and height shifts, zoom range, and shear ranges set at 0.2. As a result, an additional 1000 augmented images were generated from the original images in each category, resulting in a dataset comprising a total of 6000 augmented images (1000 per category) with a data size of 4.73 GB.

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

An artificial data set where instances belongs to several clusters with a banana shape. There are two attributes At1 and At2 corresponding to the x and y axis, respectively. The class label (-1 and 1) represents one of the two banana shapes in the dataset.

cite: Zheng Wang and Jieping Ye. Querying discriminative and representative samples for batch mode active learning. ACM Transactions on Knowledge Discovery from Data (TKDD), 9(3):1–23, 2015.

Min Wang, Ying-Yi Zhang, and Fan Min. Active learning through multi-standard optimization. IEEE Access, 7:56772–56784, 2019.

1) Data introduction • Banana dataset aims to identify good bananas based on their various variables.

2) Data utilization (1) Banana data has characteristics that: • The tabular dataset contains numerical information about bananas of different qualities (size, weight, sweetness, softness, harvest time, ripeness, acidity, quality). (2) Banana data can be used to: • Feature analysis: Researchers can analyze data sets to identify key factors affecting banana quality and provide insight into optimal growing and harvesting conditions. • Predictive modeling: Machine learning models can be trained to predict the quality of bananas based on numerical characteristics, enabling a fast and accurate classification process.

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).

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.

Fresh Banana

## Overview

Fresh Banana is a dataset for object detection tasks - it contains Banana 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).

🍌 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!