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Harumanis Mango Physical Measurements Data
Food & Beverage Consumption
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About
Physical measurement data of the Harumanis Mango is provided to enable research and development in automated fruit grading systems. The collection offers quantitative data points crucial for tasks like classifying mangoes, supporting studies in food science, and advancing artificial intelligence applications in agriculture. The measurements form the foundation for machine learning projects focused on classifying fruit quality.
Columns
- No: A unique identification number assigned to each individual mango fruit sample.
- Weight: The mass of the mango, recorded in grams (g). Measurements span from 241g up to 572g.
- Length: The measurement of the mango along its longitudinal axis, recorded in centimetres (cm). Measurements range from 10cm to 15.4cm.
- Circumference: The measurement around the widest section of the mango, recorded in centimetres (cm). Measurements range from 21.5cm to 29.9cm.
- Grade: The final quality classification assigned to the mango, categorized as Class A (51%) or Class B (49%).
Distribution
The data is typically supplied in a CSV file format, with a small size of approximately 1.43 kB. It contains 105 total tabular physical measurement records. Across the key measurement features, 67 valid entries have been confirmed. The expected update frequency for this data product is annually.
Usage
The data is perfectly suited for model building, particularly for classification tasks utilising techniques such as Support Vector Machine (SVM) and Deep Learning models. Researchers can utilise the physical attributes to predict or automate the grading of fresh fruit. It serves as a valuable resource for projects focused on quality assurance and standardising agricultural product sizes.
Coverage
The data collection is geographically focused on Malaysia, specifically sourced from the Fruit Collection Center at FAMA Perlis. The scope is limited exclusively to the physical measurements of the Harumanis Mango, identified by clone number MA 128. The coverage is non-demographic, focusing entirely on fruit characteristics.
License
Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).
Who Can Use It
- Academic Researchers: For studies cited in conference papers related to fruit grading and computer vision applications, and for general academic work or research.
- Data Scientists: For developing and testing machine learning models that predict categorical outcomes based on numerical input features.
- Agricultural Technologists: For creating innovative quality inspection tools and systems used in grading processes.
Dataset Name Suggestions
- Harumanis Mango Physical Measurements Data
- Malaysian Fruit Attribute Grading
- MA 128 Mango Classification Features
- Tabular Fruit Grading Dataset
Attributes
Original Data Source: Harumanis Mango Physical Measurements Data
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