Coffee Bean Sales Dataset - Comprehensive Insights into Coffee Orders, Customers, and Products

Description: Elevate your data-driven coffee journey with the Coffee Bean Sales Dataset, a treasure trove of information that delves into the world of coffee orders, customer profiles, and an extensive range of coffee products. Whether you're a coffee enthusiast, a data analyst, or a business owner, this dataset provides valuable insights into the coffee industry.

Contents:

Orders Worksheet:

Order ID: A unique identifier for each coffee order. Order Date: The date when the order was placed. Customer ID: An identifier linking the order to a specific customer. Product ID: A unique identifier for each coffee product. Quantity: The quantity of the coffee product ordered.

Customers Worksheet:

Customer ID: A unique identifier for each customer. Customer Name: The name of the customer. Email Address: Contact information for customers. Phone Number: Another contact detail for customers. And more: Explore a wide range of customer attributes for segmentation and analysis.

Products Worksheet:

Product ID: A unique identifier for each coffee product. Coffee Type: The type or blend of coffee, such as Arabica or Robusta. Roast Type: The roast level, including light, medium, or dark roast. Size: Information about the product size. Unit Price: The price of a single unit of the coffee product. Price Per 100g: The price per 100 grams for detailed price comparisons. Profit: Insights into the profitability of each coffee product.

Use Cases:

Market Analysis: Uncover trends in coffee consumption by analysing order patterns over time. Customer Segmentation: Create customer segments based on demographics and preferences. Product Strategy: Identify the most profitable coffee products and optimize pricing. Inventory Management: Ensure that the right quantities of each coffee product are stocked. Marketing Campaigns: Tailor marketing campaigns to specific customer segments.

Get your hands on the Coffee Bean Sales Dataset and start brewing insights today!

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Coffee fell to 286.23 USd/Lbs on August 1, 2025, down 2.90% from the previous day. Over the past month, Coffee's price has fallen 3.17%, but it is still 24.49% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Coffee - values, historical data, forecasts and news - updated on August of 2025.

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Brazil Agricultural Production: Coffee Beans data was reported at 3,405.267 Ton th in 2023. This records an increase from the previous number of 3,179.176 Ton th for 2022. Brazil Agricultural Production: Coffee Beans data is updated yearly, averaging 2,767.659 Ton th from Dec 1974 (Median) to 2023, with 50 observations. The data reached an all-time high of 4,405.416 Ton th in 1987 and a record low of 751.969 Ton th in 1976. Brazil Agricultural Production: Coffee Beans data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.RIB001: Agricultural Production.

Coffee is grown in more than 60 tropical countries on over 11 million ha by an estimated 25 million farmers, most of whom are smallholders. Several regional studies demonstrate the climate sensitivity of coffee (Coffea arabica) and the likely impact of climate change on coffee suitability, yield, increased pest and disease pressure and farmers’ livelihoods. The objectives of this paper are (i) to quantify the impact of progressive climate change to grow coffee and to produce high quality coffee in Nicaragua and (ii) to develop an adaptation framework across time and space to guide adaptation planning. We used coffee location and cup quality data from Nicaragua in combination with the Maxent and CaNaSTA crop suitability models, the WorldClim historical data and the CMIP3 global circulation models to predict the likely impact of climate change on coffee suitability and quality. We distinguished four different impact scenarios: Very high (coffee disappears), high (large negative changes), medium (little negative changes) and increase (positive changes) in climate suitability. During the Nicaraguan coffee roundtable, most promising adaptation strategies were identified, which we then used to develop a two-dimensional adaptation framework for coffee in time and space. Our analysis indicates that incremental adaptation may occur over short-term horizons at lower altitudes, whereas the same areas may undergo transformative adaptation in the longer term. At higher elevations incremental adaptation may be needed in the long term. The same principle and framework is applicable across coffee growing regions around the world.

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Use Cases for the USK-Coffee Project

1. Quality Assurance in Coffee Production

The model can be implemented to automate the process of checking coffee beans during sorting. By identifying DEFECT, LONGBERRY, PEABERRY, and PREMIUM beans, it ensures high standards in quality control. This enhances the efficiency of the sorting process, minimizes manual errors, and maintains consistent product quality.

2. Coffee Trade and Retail

Online coffee retailers can use this model to validate the type of coffee beans being sold on their platforms. This ensures that customers receive exactly what they are expecting when purchasing coffee beans. It can also assist sellers in categorizing their inventory accurately, improving customer satisfaction and trust.

3. Agriculture Research

Researchers studying coffee plants can leverage this model to visually classify different types of coffee beans. This aids in their research by speeding up the categorization process and providing more reliable data for studies related to coffee genetics, cultivation, and quality improvement.

4. Coffee Education

The model can be integrated into interactive educational platforms designed for coffee enthusiasts, baristas, and industry professionals. It helps learners distinguish between different coffee classes, enhancing their knowledge and expertise seamlessly.

5. Automated Coffee Grading

By using this model within machines designed for automated coffee grading, producers and processors can improve the efficiency and accuracy of the grading process. This automation reduces the reliance on manual inspections while maintaining or exceeding current grading standards.

Dataset File Framework

The dataset, USK-Coffee, is organized as follows:

USK-Coffee
|-- test 1600
|  |-- defect (400 Images)
|  |-- longberry (400 Images)
|  |-- peaberry (400 Images)
|  |-- premium (400 Images)
|-- train 4800
|  |-- defect (1200 Images)
|  |-- longberry (1200 Images)
|  |-- peaberry (1200 Images)
|  |-- premium (1200 Images)
|-- val 1600
|  |-- defect (400 Images)
|  |-- longberry (400 Images)
|  |-- peaberry (400 Images)
|  |-- premium (400 Images)

Dataset Annotation Team

The dataset annotations were contributed by the following team members: - Imam Sayuti, S.Tr.Kom., M.T. - Fatimah, S.T - Dr. Ir. Kahlil, S.T., M.Eng (Supervisor)

Data Source and Base

The dataset is provided by the Computer Vision Research Group (https://comvis.unsyiah.ac.id/usk-coffee/) and is based on the research paper:

Febriana, A., Muchtar, K., Dawood, R., Lin, CY. (2022, June). "USK-COFFEE Dataset: A Multi-class Green Arabica Coffee Bean Dataset for Deep Learning." In 2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom). IEEE.

Here are a few use cases for this project:

1. Coffee Quality Control: This model can be used in the coffee industry for quality control, by analyzing images of coffee beans and determining their roast types, hence ensuring consistency in coffee products.

2. Augmented Reality Shopping Assistant: It could be used in an AR application aimed at helping customers differentiate between various types of coffee at grocery stores or cafes, enhancing the shopping experience.

3. Agricultural Technology: The model can be used in ag-tech applications, helping farmers to identify and classify the state of coffee beans during the harvesting process, thereby increasing productivity and quality.

4. Coffee Machine Integration: The model could be integrated into high-end automatic coffee machines to adjust brewing parameters based on the type of coffee bean detected, thus enhancing the quality of the brewed coffee.

5. Coffee Education Applications: The model can be used in educational apps or websites about coffee, providing visual recognition and explanations about different types of coffee roasts and beans to users who are interested in learning more about coffee.

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