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.

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

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.

Mafi, Md Mafiul Hasan Matin; Sifat, R.M.; Moazzam, Md. Golam Moazzam; Uddin, Mohammad Shorif (2023), “Banana Disease Recognition Dataset”, Mendeley Data, V1, doi: 10.17632/79w2n6b4kf.1

Banana

## Overview

Banana is a dataset for object detection tasks - it contains Bananas annotations for 1,625 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.

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.

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.

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.

Banana Disease Identification

## Overview

Banana Disease Identification is a dataset for classification tasks - it contains Banana Leaf Parts annotations for 5,360 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 Quality Detection

## Overview

Banana Quality Detection is a dataset for object detection tasks - it contains Quality annotations for 282 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 Classification

## Overview

Banana Classification is a dataset for classification tasks - it contains Agriculture annotations for 562 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).

Banana Machine Learning

## Overview

Banana Machine Learning is a dataset for object detection tasks - it contains Pisang annotations for 200 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).

This dataset contains colour images of top fruit types with two quality labels: Good and Bad. It includes six classes: Apple_Good, Apple_Bad, Banana_Good, Banana_Bad, Lime_Good, Lime_Bad. The approximate sample counts per class are:

• Apple_Bad: 1141

• Apple_Good: 1134

• Banana_Bad: 1087

• Banana_Good: 1113

• Lime_Bad: 1085

• Lime_Good: 1094

The images were captured under varying lighting conditions, backgrounds and viewpoints, using high-resolution mobile phone cameras, both indoor and outdoor. The dataset is derived from the FruitNet dataset (Meshram et al., 2022) which originally included six fruits (apple, banana, guava, lime, orange, pomegranate) and three quality labels (Good/Bad/Mixed) with ~19 500 images.

In this version we have selected the six classes (three fruits × two quality levels) to provide a balanced corpus of ~ 6,700 images. Each image is stored at 256×256 resolution (or rescaled to this size) and labelled with the class name.

Intended Use:
This dataset is suitable for research in computer vision, particularly fruit quality classification, defect detection, visual sorting in agriculture, and related machine learning tasks.

Source / Reference:
Meshram V. A., Patil K., Ramteke S. D. “MNet: A Framework to Reduce Fruit Image Misclassification” (2021) IIETA. DOI: 10.18280/isi.260203. Dataset details: top Indian fruits, Good/Bad labels, ~12,000 images. (https://www.iieta.org/journals/isi/paper/10.18280/isi.260203?utm_source=chatgpt.com">IIETA)

Dataset structuring:

• Directory per class (e.g. Apple_Bad/)

• JPEG or PNG images

• Recommended preprocessing: resizing to 256×256, normalising pixel values, optional data augmentation

lllab/lllab-vision-banana-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

16-Bytes/banana dataset hosted on Hugging Face and contributed by the HF Datasets community

Top Banana

## Overview

Top Banana is a dataset for instance segmentation tasks - it contains Banana annotations for 732 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).

Discover the booming global banana market! This in-depth analysis reveals key trends, growth drivers, and market size projections (2025-2033), highlighting opportunities and challenges in this multi-billion dollar industry. Learn about regional market shares and leading producers.

This statistic reflects the global banana production by region in 2024. In Asia, some 73.05 million metric tons of bananas were produced in that year.

Good Banana: Imagine a banana with a vibrant yellow peel, perhaps with a few tiny brown speckles – these are often called "sugar spots" and indicate peak sweetness and ripeness. It feels firm to the touch but yields slightly under gentle pressure. The aroma is sweet and fruity. Inside, the flesh is creamy, smooth, and a pale ivory color, offering a delightful balance of sweetness and a subtle tang. A good banana is easy to peel, with no bruising or soft spots, and provides a burst of energy and essential nutrients like potassium. Bad Banana: On the other hand, a "bad" banana might sport a predominantly brown or black peel, signaling overripeness and potential spoilage. It could feel mushy or overly soft to the touch, possibly even leaking. The smell might be overly fermented or unpleasant. Inside, the flesh could be discolored, watery, or have a fermented taste. Bruises, cuts, or mold growth also clearly indicate a banana past its prime. While still potentially usable in baking in some cases, a bad banana in this context is generally undesirable for fresh consumption.