The occurrence of poultry diseases not only affects farm production economics but also leads to poor poultry welfare, food safety concerns, and zoonotic infections. Therefore, timely detection of these diseases is of paramount importance in poultry production. This study proposes a machine vision-based monitoring system for broiler chicken as they walk through a test area. Data were collected from two groups of broilers; the control group and treatment group (inoculated intramuscularly with virulent Newcastle disease virus) housed in fully isolated chambers for comparative monitoring. The broilers were monitored by video surveillance for data labelling and depth camera for the automated health status classifier development. Feature variables were extracted based on 2D posture shape descriptors (circle variance, elongation, convexity, complexity, and eccentricity) and mobility feature (walk speed). A statistical analysis of the feature variables established that all investigated features were statistically significant with time after challenge in the treatment group. The earliest possible infection detection time was on the 4th day based on circle variance and elongation, and the 6th day based on eccentricity and walk speed. However, convexity and complexity could not provide early detection. Two sets of classifiers were then developed based on only the posture shape descriptors, and on all the feature variables, The Support Vector Machine (RBF-SVM) outperformed all the other models with an accuracy of 0.975 and 0.978 respectively. The proposed system can serve as an automatic broiler monitoring system by providing an early warning and prediction of an occurrence of disease continuously and non-intrusively

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