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.

SVM

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.

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 • 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.

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 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.

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.

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.

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.

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.

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

Banana Market Outlook

According to our latest research, the global banana market size reached USD 137.2 billion in 2024, and the industry is poised for robust expansion with a projected compound annual growth rate (CAGR) of 4.1% from 2025 to 2033. By the end of the forecast period, the global banana market is anticipated to reach USD 194.5 billion. This growth trajectory is primarily driven by the rising demand for nutritious, affordable, and convenient fruit options across both developed and emerging economies, coupled with the increasing application of bananas in diverse sectors such as food and beverages, pharmaceuticals, and cosmetics.

One of the key growth factors propelling the banana market is the fruit’s widespread acceptance as a staple in daily diets worldwide, owing to its high nutritional value, affordability, and year-round availability. Bananas are rich in essential nutrients such as potassium, vitamin C, and dietary fiber, making them a preferred choice for health-conscious consumers. The growing trend towards plant-based and functional foods is further boosting banana consumption, especially in urban centers where convenience and health benefits are prioritized. Additionally, the versatility of bananas—consumed fresh, processed, or as an ingredient in various products—has significantly expanded their market reach, supporting sustained demand across regions and demographics.

Another significant factor fueling the banana market is the increasing adoption of bananas in processed forms, including banana chips, purees, flours, and beverages. The food and beverage industry is leveraging the unique flavor profile and nutritional attributes of bananas to develop innovative products that cater to evolving consumer preferences. Simultaneously, the pharmaceutical and nutraceutical sectors are exploring banana-derived compounds for their potential health benefits, such as prebiotic properties and antioxidant activity. Moreover, the cosmetic industry is incorporating banana extracts in skincare and haircare formulations, capitalizing on their moisturizing and nourishing properties. These diverse applications are creating new avenues for market growth and value addition.

Technological advancements and improvements in supply chain management have also played a pivotal role in the expansion of the banana market. Enhanced cultivation techniques, better disease management, and efficient logistics have contributed to increased yields and reduced post-harvest losses. The proliferation of organized retail and e-commerce platforms has further facilitated the easy availability of bananas and banana-based products to a broader consumer base. Furthermore, government initiatives supporting sustainable agriculture and fair trade practices are fostering a favorable environment for banana producers, particularly in key exporting countries. These collective efforts are instrumental in meeting the growing global demand while ensuring quality and sustainability.

Regionally, Asia Pacific continues to dominate the global banana market, accounting for the largest share both in terms of production and consumption. Countries such as India, China, and the Philippines are major contributors, leveraging favorable climatic conditions and large-scale cultivation. Latin America, led by Ecuador, Colombia, and Costa Rica, remains the primary exporter of bananas, supplying to North America and Europe, where demand for fresh and processed bananas is steadily rising. The Middle East & Africa region is witnessing increased consumption due to changing dietary patterns and urbanization, while North America and Europe are experiencing growth driven by health and wellness trends. These regional dynamics underscore the global interconnectedness and diverse opportunities within the banana market.

Product Type Analysis

The banana market is broadly segmented by product type into fresh bananas, processed bananas, organic bananas, and conventional bananas. Fresh bananas represent the largest s

The Banana Market report segments the industry into Geography (North America, Europe, Asia-Pacific, South America, Africa). The report includes Production Analysis, Consumption Analysis by Value and Volume, Import Analysis by Value and Volume, Export Analysis by Value and Volume, and Price Trend Analysis. Five years of historical data and five-year forecasts are provided.

Shelf Life Of Banana

## Overview

Shelf Life Of Banana is a dataset for object detection tasks - it contains Banana annotations for 1,595 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).

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.

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In 2023, the per capita availability of fresh bananas for consumption in the United States amounted to 26.7 pounds. This represents an increase of 0.4 percent compared to the previous year.