<![CDATA[Purpose – This study investigates how different image segmentation techniques influence machine-learning performance in cow pose classification and identifies an effective combination of preprocessing, segmentation, and classification methods for image-based livestock monitoring. Design/methods/approach – A publicly available dataset comprising 271 two-dimensional RGB cow images was classified into four pose categories: front, left, right, and others. Images were processed using brightness adjustment and Gaussian blur, followed by Canny edge detection, Laplacian filtering, or K-Means clustering. Support Vector Machine (SVM) and Random Forest (RF) classifiers were then evaluated using 10-fold cross-validation. Model performance was assessed using accuracy, precision, recall, F1-score, and receiver operating characteristic–area under the curve (ROC-AUC). Findings – The K-Means-based segmentation configurations consistently outperformed Canny- and Laplacian-based approaches. Random Forest combined with K-Means achieved the best overall performance, with 99.60% accuracy, 98.99% precision, 99.59% recall, and a 98.99% F1-score. The corresponding K-Means-based SVM model achieved 98.16% accuracy. In contrast, Laplacian-based configurations produced the lowest classification accuracy, ranging from approximately 64% to 65%. These findings indicate that segmentation quality substantially affects the discriminative representation available to conventional machine-learning classifiers. Research implications/limitations – The findings demonstrate the potential of combining relatively simple segmentation and machine-learning methods for cow pose recognition when dataset size and computational resources are limited. However, the study is restricted to a relatively small publicly available image dataset and four pose categories; therefore, validation using larger, more heterogeneous, farm-based datasets is required before practical deployment. Originality/value – This study provides a comparative evaluation of multiple classical segmentation techniques under a consistent machine-learning framework and highlights the effectiveness of K-Means segmentation combined with Random Forest for cow pose classification.]]>
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