Explainable Machine Learning Framework for Milk Quality Grading

Year 2025, Volume: 18 Issue: 3, 227 - 235, 03.10.2025

Bekir Çetintav , Ahmet Yalçın

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

Milk quality , Machine learning , Explainable AI (XAI) , Dairy Industry , Veterinary Food Safety

Süt Kalitesi Derecelendirmesi için Açıklanabilir Makine Öğrenimi Çerçevesi

Abstract

Bu çalışma, süt kalitesinin değerlendirilmesinde yüksek tahmin doğruluğunu şeffaflık ve kullanışlılık ile birleştiren açıklanabilir bir makine öğrenimi yaklaşımı sunmaktadır. Random Forest ve HistGradientBoost modellerinin yanı sıra Permutasyon Feature Importanee ve LIME gibi yorumlanabilirlik tekniklerini kullanan bu yaklaşım, güçlü bir sınıflandırma performansı sağlarken uygulanabilir içgörüler de sunmaktadır. Global yorumlanabilirlik sonuçları, pH ve Sıcaklık gibi kritik faktörleri belirleyerek gerçek zamanlı izleme ve mikrobiyal kontroldeki önemlerini vurgulamaktadır. Yerel yorumlanabilirlik sonuçları ise, sunulan 2 örnek üzerinden, bireysel tahminlerin pratik faydasını göstererek depolama koşullarının optimize edilmesi veya kontaminasyon risklerinin ele alınması gibi hedefe yönelik müdahalelere olanak tanımaktadır. Tahmin doğruluğu ile yorumlanabilirlik arasındaki boşluğu kapatan bu yaklaşım, yalnızca paydaşlar için güven ve kullanılabilirliği artırmakla kalmayıp, aynı zamanda AI destekli kalite kontrol sistemlerinin süt endüstrisine entegrasyonu için yeni bir perspektif sunmaktadır.

Keywords

Süt kalitesi , Makine öğrenimi , Açıklanabilir Yapay Zeka (XAI) , Süt endüstrisi , Veteriner gıda güvenliği

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