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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.

'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 THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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.

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.

Banana Ripening Process Dataset and Model

This dataset contains images of the classes below:

• freshripe
• freshunripe
• overripe
• ripe
• rotten
• unripe

Usage

This is an object detection model that can be used to possibly identify where in the Fruit Ripening Process fruit at stores are and when to take them off the shelves and put them in composting.

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 THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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

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).

Report Features	Details
Product Name	Banana
Industrial Uses	Alcohol Production, Banana chips, Banana processing, Cosmetics and skincare, Animal feed
HS Code	08039010
Supplier Database	Fresh Del Monte, Chiquita Brands International Sarl, Fyffes, Dole Food Company, Reybanpac
Region/Countries Covered	Asia Pacific: China, India, Indonesia, Pakistan, Bangladesh, Japan, Philippines, Vietnam, Iran, Thailand, South Korea, Iraq, Saudi Arabia, Malaysia, Nepal, Taiwan, Sri Lanka, UAE, Israel, Hongkong, Singapore, Oman, Kuwait, Qatar, Australia, and New Zealand Europe: Germany, France, United Kingdom, Italy, Spain, Russia, Turkey, Netherlands, Poland, Sweden, Belgium, Austria, Ireland Switzerland, Norway, Denmark, Romania, Finland, Czech Republic, Portugal and Greece North America: United States and Canada Latin America: Brazil, Mexico, Argentina, Columbia, Chile, Ecuador, and Peru Africa: South Africa, Nigeria, Egypt, Algeria, Morocco
Currency	US$ (Data can also be provided in local currency)
Supplier Database Availability	Yes
Customization Scope	The report can be customized as per the requirements of the customer
Post-Sale Analyst Support	360-degree analyst support after report delivery

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} }

The raster dataset consists of a 500m score grid for banana storage location achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Banana. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure.

It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Major Cities Accessibility" * 0.2) + (“Asset Wealth” * 0.1) + ("Major Ports Accessibility" * 0.1).

This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location).

Data publication: 2021-11-17

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Dariia Nesterenko

Data lineage:

Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets: 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. Altas AI - Asset Wealth Index 2020.

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO)

Online resources:

Zipped raster TIF file for Angola Crop Storage Location Score: Banana (Angola - ~ 500m)

The Market for Banana Flour can be expected to expand at a value based CAGR of 5.4% and show an increase in revenue from US$ 730.2 Million to around US$ 1,235.5 Million by 2033.

This statistic shows the import volume of fresh bananas to the United States from 2010 to 2022. In 2022, approximately 10.15 billion pounds of bananas were imported to the United States.

The banana flour market is set for significant expansion, with an anticipated valuation of US$ 628.7 million by 2024. The market displays a considerable trend, featuring a CAGR of 3.6%, expected to endure until 2034. The consistent growth forecasts indicate that the global market is poised to achieve an impressive valuation of US$ 892.8 million by 2034.

Attributes	Key Insights
Market Estimated Size in 2024	US$ 628.7 million
Projected Market Size in 2034	US$ 892.8 million
Value-based CAGR from 2024 to 2034	3.6%

2019 to 2023 Historical Analysis vs. 2024 to 2034 Market Forecast Projections

Historical CAGR (2019 to 2023)	3.4%
Forecasted CAGR (2024 to 2034)	3.6%

Country-wise Insights

The United States	2.7%
The United Kingdom	2.5%
China	3.7%
Germany	3.2%
India	4.3%

Category-wise Insights

Category	Market Share in 2024
Organic	97.3%
Food Industry	61.3%

Report Scope

Attributes	Details
Estimated Market Size in 2024	US$ 628.7 million
Projected Market Valuation in 2034	US$ 892.8 million
CAGR Share from 2024 to 2034	3.6%
Forecast Period	2024 to 2034
Historical Data Available for	2019 to 2023
Market Analysis	Value in US$ million
Key Regions Covered	North America Latin America Western Europe Eastern Europe South Asia and Pacific East Asia Middle East and Africa
Key Market Segments Covered	By Process By Application By End-user By Source By Region
Key Companies Profiled	NuNatural Inc. Zuvii NOW Health Group Inc. Natural Evolution Ceres Enterprises Ltd Diana Foods International Agriculture Group ADM Wild Europe GmbH & Co. KADAC Pty Ltd

• Banana leaves are susceptible to several diseases, which have a significant impact on their yield. These diseases can cause damage to banana plants, resulting in reduced fruit production, stunted growth, and even plant death. As a result, affected plants are often unable to produce marketable fruit, leading to economic losses for banana farmers and potentially affecting the overall global banana supply.
• The dataset contains images of 3 prominent banana leaf spot diseases: (a) Sigatoka (b) Cordana and (c) Pestalotiopsis. It also contains images of healthy leaves.
• The dataset is split into 2 sets: (a) Original Set and (b) Augmented Set.
• The raw set contains 937 RGB images of four classes in JPG format.
• The augmented set contains 400 images for each class, totalling 1600 images. The following augmentation operations are performed: gaussian blur, horizontal flip, crop, linear contrast, shear, translate, and rotate shear. All images have a standard resolution of 224 x 224 pixels.
• All images are captured using smartphone camera from banana fields of Bangabandhu Sheikh Mujibur Rahman Agricultural University, Bangladesh and adjacent banana fields during June 2021. Three smartphone cameras were used to collect the data. All images are labelled accordingly by a plant pathologist.

Banana Price in the United States - 2023. Find the latest marketing data on the IndexBox platform.

Banana price (USA) in , March, 2024 For that commodity indicator, we provide data from January 1960 to March 2024. The average value during that period was 0.54 USD per kilogram with a minimum of 0.11 USD per kilogram in January 1968 and a maximum of 1.68 USD per kilogram in December 2022. | TheGlobalEconomy.com

The Organic Banana Market is segmented by an Analysis of the Production (Volume), Consumption (Value and Volume), Trade in Terms of Import (Value and Volume) and Export (Value and Volume), and Price Trend of Organic Bananas. The Market is Segmented by Geography (Dominican Republic, Ecuador, Colombia, Philippines, Honduras, South Africa, Peru, United States, Japan, and Costa Rica). The market size and forecasts by volume (in metric ton) and value (in USD thousand) for the above segments.

The report covers Global Fresh Banana Market Production & Demand and Includes Production Analysis (Volume), Consumption Analysis (Volume and Value), Trade in Terms of Import Analysis (Volume and Value), Export Analysis (Volume and Value), and Price Trend Analysis. The Market is Segmented by Geography (North America, Europe, Asia-Pacific, South America, and Africa). The report offers market size and forecasts for all the above segments in volume (ton) and value (USD million).

The global banana puree market is predicted to increase from a value of US$ 1.33 Bn in 2022 to US$ 2.24 Bn by 2032, expanding at a CAGR of 5.3% over the said period.