Separating Chickens' Heads and Legs in Thermal Images via Object Detection and Machine Learning Models to Predict Avian Influenza and Newcastle Disease.

Poultry body temperature is closely related to their metabolism and vital activities, which can indicate their physiological status and health. Therefore, monitoring these temperature changes by analyzing thermal images can help in the early and accurate diagnosis of their diseases using a non-destructive method. On the other hand, it is very important to state which part of the bird has the greatest effect on the diagnosis of the disease. This not only speeds up the diagnosis process but also determines an important index for animal pathologists. In this study, an intelligent algorithm was presented with the aim of early diagnosis and classification of two diseases, Avian influenza and Newcastle disease, in the early hours of disease transmission. For this purpose, three different models were developed based on thermal images, including: original images, images with background removal, and images with the head and legs of the chicken separated by the YOLO-v8 model. Then, the features extracted from the thermal images, including texture and color, were evaluated in all three models with a support vector machine (SVM) classifier. Also, the most important and effective features of thermal images for the diagnosis of two diseases, Avian influenza and Newcastle disease, were introduced to other researchers by the Relief feature selection algorithm. The classification results of the original images, images without background and images of the head and legs of chickens for Avian influenza were 75.89, 83.93, and 92.48%, respectively, and for Newcastle disease were 83.04, 91.52, and 94.20% respectively. The model developed for early diagnosis of the disease showed the ability to diagnose the two diseases at 8 h after disease infection with an accuracy of more than 90%. The results show that the contribution of texture-related features is greater than other features extracted from thermal images in the diagnosis of poultry diseases. Also, focusing on the head and feet areas by the YOLO-v8 algorithm will increase the classification accuracy, which allows for more accurate diagnosis in real time and in the early stages of the disease.