This is a dataset for detecting banana quality using ML. This dataset contains four categories: Unripe, Ripe, Overripe and Rotten. In this dataset, there are enormous amount of images which will help users to train the ML model conveniently and easily.

NOTE: THIS DATASET HAS BEEN PICKED FROM https://universe.roboflow.com/roboflow-universe-projects/banana-ripeness-classification. I WAS FACING DIFFICULTIES WHILE DOWNLOADING DATASET DIRECTLY TO THE GOOGLE COLAB TO TRAIN MY CNN MODEL AS A PART OF UNIVERSITY PROJECT. ALL CREDITS FOR THIS DATASET, AS FAR AS MY KNOWLEDGE GOES, GOES TO ROBOFLOW. I DO NOT INTEND TO TAKE ANY CREDITS MYSELF OR UNETHICALLY CLAIM OWNERSHIP, I JUST UPLOADED DATASET HERE FOR MY CONVENIENCE, THANK YOU.

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.

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

Our general objective is that the dataset proposed here be useful for the development of Machine Learning and Computer Vision algorithms whose central object of analysis is the banana. The images contained here are of bananas from the Prata Catarina cultivar, with labeling of eight classes that represent levels of control of the fruit. For the labeling process, they were labeled via Bounding box, demarcating the banana in the image and assigning it a degree of maturation following the norms proposed in CEAGESP (2006). All 1000 images of bananas were taken using only smartphones. These images were collected on February 4th, 13th and 17th, 2023, with variations of the background on a smooth surface (white marble), on clayey soil or on foliage. Lastly, all data was uploaded to the roboflow platform and labeled using the bounding boxes method.

SVM

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.

Banana Project

## Overview

Banana Project 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).

This dataset is curated to support research in banana crop health, focusing on the classification and detection of diseases affecting banana leaves and fruits. It is designed for machine learning and deep learning applications, including image classification and computer vision-based disease diagnosis.

★Dataset Overview: The dataset includes both raw and augmented images across various categories, covering healthy and diseased banana leaves and fruits. The diseases featured include Anthracnose, Banana Fruit-Scarring Beetle, Banana Skipper Damage, Banana Split Peel, Black and Yellow Sigatoka , Chewing insect damage on banana leaf, Healthy Banana, Healthy Banana leaf and Panama Wilt Disease.

★Composition: Raw Data: 2375 images Augmented Data: 9513 images

★Applications: This dataset is valuable for:

*Machine Learning & AI Research – Training models for automated disease detection.

*Agricultural Studies – Assisting researchers in understanding banana crop health.

*Farmers & Agricultural Experts – Enabling early disease identification for better crop management.

By providing a comprehensive collection of banana and banana leaf conditions, this dataset serves as an essential resource for advancing smart agriculture and precision farming techniques. This dataset isn't just for researchers; it’s also a valuable tool for farmers and agricultural specialists who want to identify diseases early and take action to protect their crops. By using this data, we can work towards smarter farming, healthier plants, and better food security.

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.

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.

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

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.

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.

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 Varieties

## Overview

Banana Varieties is a dataset for object detection tasks - it contains Banana Varieties annotations for 1,348 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 • 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.

failproof/banana dataset hosted on Hugging Face and contributed by the HF Datasets community

0xndika/Banana dataset hosted on Hugging Face and contributed by the HF Datasets community

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