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Dergiler / Black Sea Journal of Agriculture / 2026 / Cilt: 9 Sayı: 4

Statistical Evaluation of Meat Quality in Broiler Chickens Using a Fuzzy Logic–Based Decision Support System

Lütfi Bayyurt(TOKAT GAZIOSMANPASA UNIVERSITY FACULTY OF AGRICULTURE DEPARTMENT OF ZOOTECHNICS)0000-0003-2613-9302
Emine Berberoğlu(TOKAT GAZİOSMANPAŞA ÜNİVERSİTESİ, ZİRAAT FAKÜLTESİ, ZOOTEKNİ BÖLÜMÜ, ZOOTEKNİ PR.)0000-0002-7318-2728
Sayfa
543–552
Yayın Tarihi
2026-07-15
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Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Overall, the developed fuzzy logic–based model effectively addresses the complexity and vagueness of meat quality prediction, providing reliable classifications that are simple to interpret.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Traditional methods are limited by the accuracy and objectivity of broiler meat quality evaluation, due to both the complexity of its multivariate structure and conflict inherent in the uncertainty of data.
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Temel Bulgular & Veri:A Mamdani-type fuzzy logic-based decision support system was designed for meat quality classification based on the dataset of 48 male Ross 308 broiler chickens, slaughtered at the age of 42 days.
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Sonuç & Katkı:Overall, the developed fuzzy logic–based model effectively addresses the complexity and vagueness of meat quality prediction, providing reliable classifications that are simple to interpret.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Fuzzy logic · Decision support system · Broiler chickens

Özet

Traditional methods are limited by the accuracy and objectivity of broiler meat quality evaluation, due to both the complexity of its multivariate structure and conflict inherent in the uncertainty of data. A Mamdani-type fuzzy logic-based decision support system was designed for meat quality classification based on the dataset of 48 male Ross 308 broiler chickens, slaughtered at the age of 42 days. In this structure, pH24, cooking loss (CL), drip loss (DL) and lightness (L*) were used as input variables, and quality was classified into low/high/medium groups as output variable. The model performance was thoroughly evaluated by examining the results against those provided by human experts, using Accuracy, Cohen’s kappa, and Weighted kappa statistics to achieve meaningful measures. Results indicated that the Accuracy rate attained by the model was between 81% to 85%, along with a Kappa coefficient of over 0.70, evidencing moderate agreement. Importantly, the weighted kappa analysis demonstrated that most classification errors happened between adjacent quality classes. Overall, the developed fuzzy logic–based model effectively addresses the complexity and vagueness of meat quality prediction, providing reliable classifications that are simple to interpret.

Abstract

Traditional methods are limited by the accuracy and objectivity of broiler meat quality evaluation, due to both the complexity of its multivariate structure and conflict inherent in the uncertainty of data. A Mamdani-type fuzzy logic-based decision support system was designed for meat quality classification based on the dataset of 48 male Ross 308 broiler chickens, slaughtered at the age of 42 days. In this structure, pH24, cooking loss (CL), drip loss (DL) and lightness (L*) were used as input variables, and quality was classified into low/high/medium groups as output variable. The model performance was thoroughly evaluated by examining the results against those provided by human experts, using Accuracy, Cohen’s kappa, and Weighted kappa statistics to achieve meaningful measures. Results indicated that the Accuracy rate attained by the model was between 81% to 85%, along with a Kappa coefficient of over 0.70, evidencing moderate agreement. Importantly, the weighted kappa analysis demonstrated that most classification errors happened between adjacent quality classes. Overall, the developed fuzzy logic–based model effectively addresses the complexity and vagueness of meat quality prediction, providing reliable classifications that are simple to interpret.

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Bu Makaleye Atıf Yap

Bayyurt, L., Berberoğlu, E. (2026). Statistical Evaluation of Meat Quality in Broiler Chickens Using a Fuzzy Logic–Based Decision Support System. Black Sea Journal of Agriculture, Cilt: 9 Sayı: 4, 543–552. https://doi.org/10.47115/bsagriculture.1923179

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