Explainable Machine Learning Framework for Milk Quality Grading

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Kocatepe Veteriner Dergisi
Cilt: 18 Sayı: 3
Explainable Machine Learning Framework for Milk Quality Grading

Explainable Machine Learning Framework for Milk Quality Grading

Authors : Bekir Çetintav, Ahmet Yalçın

Pages : 227-235

View : 75 | Download : 125

Publication Date : 2025-10-03

Article Type : Research Paper

Abstract :This study introduces an explainable machine learning framework for milk quality grading, combining high predictive performance with transparency and practicality. Utilizing Random Forest and HistGradientBoost models, alongside interpretability techniques like Permutation Feature Importance and LIME, the framework achieves robust classification while providing actionable insights. Global explanations identify pH and Temperature as critical factors, highlighting their significance in real-time monitoring and microbial control. Local explanations, based on the two presented examples, demonstrate the practical utility of individual predictions, offering targeted interventions such as optimizing storage conditions or addressing contamination risks. By bridging the gap between predictive accuracy and interpretability, this framework not only enhances trust and usability for stakeholders but also establishes a new perspective for integrating AI-driven quality control systems into the dairy industry.
Keywords : Süt kalitesi, Makine öğrenimi, Açıklanabilir Yapay Zeka (XAI), Süt endüstrisi, Veteriner gıda güvenliği

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