PERFORMANCE OF MILK QUALITY DIAGNOSTICS USING EXTRA TREE CLASSIFIER TECHNIQUES WITH PROGRESSIVE LEARNING
Pham Hoang Minh, Truong Hung Chen, Pham Huynh Thuy An, Ngo Ho Anh Khoi
Abstract: Quality control of milk involves the use of established control measures and testing methods to ensure proper adherence to standards and regulations concerning milk and its products. Testing ensures that dairy products meet the requirements of standards, are acceptable in terms of nutritional content, and adhere to safety standards regarding microbiological factors, heavy metals, pesticide residues, veterinary drug residues, toxins, and more. Therefore, quality checks at various stages of the milk processing chain, from farms to processing facilities and consumers, are crucial. The research methodology for this project involves scientific experimentation, conducted using the Extra Tree Classifier algorithm with evolving method. The scope of the project is not extensive, and the dataset utilized is the Milk Quality Prediction dataset sourced from kaggle.com. The aim of the project is to aid in diagnosing milk quality rapidly and relatively reliably through provided numerical data. This endeavor aims to reduce the prevalence of low-quality milk trading, ultimately contributing to safer and higher quality milk management for consumers.
Keywords: Milk Quality Prediction, Milk Quality Diagnosis, Machine Learning, AI, Extra Tree Classifier, Progressive Learning
Milk Quality Attributes
| Name | Description | Range/Options |
|---|---|---|
| pH (pH level) | Represents the pH level of the milk | 0 – 10 (3 - 9.5) |
| Temperature | Describes the temperature of the milk | 0 – 100 (34 - 90) |
| Taste | Indicates the taste of the milk | 0 (poor) or 1 (good) |
| Odor | Indicates the odor of the milk | 0 (poor) or 1 (good) |
| Fat | Represents the fat content of the milk | 0 (low) or 1 (high) |
| Turbidity | Indicates the turbidity of the milk | 0 (low) or 1 (high) |
| Colour | Describes the color of the milk | 240 – 255 |
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