'Mango and Banana Dataset (Ripe Unripe)' is the RGB image dataset. This dataset of 5000 photos of bananas and mangoes focuses on identifying ripe and unripe fruits. Each photograph has metadata that identifies whether or not the banana in the image is considered ripe. The data set was gathered in indoor as well as outdoor lighting conditions, to identify ripe and unripe Bananas and Mangoes. Each image in this dataset has a YOLO.txt label attached to it. This data can be used to train all YOLO Object Detection models. The dataset has been divided into two sections: Train and Test each of which contains 80% and 20% of the total data. Train folder contains 4000 images with labels and Test folder contains 1000 images with labels. The purpose of collecting this dataset was to create 'Ripe Unripe Fruit Detection System' using YOLOv8 Object detection model. Dimensions of image : 640 x 480

Dataset

This dataset was created by Saravanan Jaichandaran

Released under CC0: Public Domain

Contents

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.

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.

The banana images dataset was created to contribute to the study of banana diseases diagnostics. The images target the diagnostics of Black Sigatoka and Fusarium Wilt Race 1 diseases. We are motivated in developing end to end tools to help farmers diagnose diseases and improve banana productivity. The dataset was created to facilitate image classification and object detection tasks.

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.

TCleo/3-banana-maturity dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset of 1340 photos of bananas focuses on identifying ripe and unripe fruit. Each photograph has metadata that identifies whether or not the banana in the image is considered ripe. The data set was gathered in indoor lighting circumstances , to identify ripe and unripe bananas. Each image in this dataset has a YOLO.txt label attached to it. This data can be used to train all YOLO Object Detection models. The dataset has been divided into three sections: Train, Test, and Validation, each of which contains 65%, 20%, and 15% of the total data.

This dataset of 5000 photos of bananas and mangoes focuses on identifying ripe and unripe fruits. Each photograph has metadata that identifies whether or not the banana in the image is considered ripe. The data set was gathered in indoor as well as outdoor lighting conditions, to identify ripe and unripe Bananas and Mangoes. Each image in this dataset has a YOLO.txt label attached to it. This data can be used to train all YOLO Object Detection models. The dataset has been divided into two sections: Train and Test each of which contains 80% and 20% of the total data. Train folder contains 4000 images with labels and Test folder contains 1000 images with labels. Dimensions of image : 640 x 480

The Story of a Banana Peel map journal from the "DCPS Recycles! School Waste Story Map" series. Have you ever wondered how a banana got to your tray? Bananas do not grow in most parts of the US. It takes about one year for a banana that you eat in school to make it to your tray. Where might it have come from? What happens to the banana peel when you discard it? Follow the journey of this banana peel to explore the story of a banana before and after it is eaten.The DCPS Recycles! program is designed to fulfill legal requirements; improve building operations; reduce waste of money and natural resources; achieve the SustainableDC target of zero waste by 2032; and teach DCPS students values and skills for a sustainable 21st century. The goal is to sort all waste properly so as much as possible can be composted or recycled instead of sent to a landfill or incinerator! Agency Website.

Dataset

This dataset was created by Kousik V 22MCR055

Contents

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

A collection of resources for the economics of banana production.

Get the latest insights on price movement and trend analysis of Banana in different regions across the world (Asia, Europe, North America, Latin America, and the Middle East & Africa).

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.

This statistic shows the value of U.S. imports of bananas worldwide in 2023, by country of origin. That year, the United States received its largest amount of banana imports from Guatemala, valued at 1.2 billion U.S. dollars.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16850153%2Fc03339173192628883948f86f9b1c742%2Funtitled.png?generation=1730970274895169&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.

This statistic shows the import volume of fresh bananas to the United States from 2010 to 2023. In 2023, approximately 10.24 billion pounds of bananas were imported to the United States.

Fusarium wilt of banana, also known as "banana cancer", currently threatens banana production areas worldwide. Timely and accurate identification of Fusarium wilt disease is crucial for effective disease control and optimizing agricultural planting structure. To explore the use of unmanned aerial vehicle (UAV) remote sensing for identifying banana wilt disease, this study obtained comprehensive experimental data on wilted banana plants in a banana plantation in Long'an County, Guangxi. The data set includes UAV multispectral reflectance data and ground survey data on the incidence of banana wilt disease. The UAV multispectral imaging data have high spatial resolution, while the ground survey data is accurate, making them suitable for research on UAV remote sensing identification and monitoring of banana wilt disease. This data set is of great significance for promoting research and application of remote sensing monitoring of crop diseases.

Banana Price in the United States - 2024. Find the latest marketing data on the IndexBox platform.