Performance of milk quality diagnostics using extra tree classifier techniques with progressive learning

Tóm tắt

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 method involved scientific experimentation, conducted using the Extra Tree Classifier algorithm with evolving method. The scope of the study was not extensive, and the dataset was the Milk Quality Prediction dataset sourced from kaggle.com. The aim of the study was 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.