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Siirt Üniversitesi, Ziraat Fakültesi, Zootekni Bölümü, Siirt
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Çukurova Üniversitesi, Ziraat Fakültesi, Zootekni Bölümü, Adana
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Çukurova Üniversitesi, Ziraat Fakültesi, Zootekni Bölümü, Adana
Abstract
Machine learning is increasingly being applied in agriculture and animal husbandry, offering substantial advantages in identifying patterns within datasets, predicting outcomes, and detecting diseases and pests. In the livestock sector, these methods are also widely used for quality control, classification, and yield prediction of milk and dairy products. This study presents a meta-analysis focusing on the application of the Random Forest (RF) algorithm—one of the widely used machine learning methods—in datasets related to milk and dairy products published between 2020 and 2025. A total of 15 research articles were systematically reviewed using the Web of Science (WoS) and ScienceDirect databases, based on the keywords “machine learning,” “Random Forest,” “milk,” “dairy products,” and “classification.” Review articles, books, and conference proceedings were excluded from the analysis. Effect sizes were calculated based on accuracy values and subsequently standardized using the Freeman–Tukey double arcsine transformation to ensure suitability for meta-analysis. Study heterogeneity was assessed using Cochran’s Q and I² statistics. A random-effects model was adopted to evaluate and report the performance of the Random Forest algorithm. The effect sizes of individual studies were illustrated using forest plots, while publication bias was assessed through funnel plots and relevant statistical tests, including Egger’s regression and Kendall’s Tau.
Keywords
Milk and dairy products,accuracy value,random forest algorithm,meta-analysis,machine learning
How to Cite
CERİTOĞLU, F., ÇELİK GÜNEY, M., & CEBECİ, Z. (2025). A Meta-Analytic Evaluation of the Use of Machine Learning in Milk and Dairy Products. ISPEC Journal of Agricultural Sciences, 9(4), 1223–1232. https://doi.org/10.5281/zenodo.17931309
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Melis ÇELİK GÜNEY
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