Ansarimovahed Alireza, Banakar Ahmad, Li Guoming, Javidan Seyed Mohamad
Biosystems Engineering Department, Tarbiat Modares University, Tehran P.O. Box 14115-111, Iran.
Department of Poultry Science, University of Georgia, Athens, GA 30602, USA.
Animals (Basel). 2025 Apr 11;15(8):1114. doi: 10.3390/ani15081114.
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.
家禽体温与它们的新陈代谢和生命活动密切相关，能够表明其生理状态和健康状况。因此，通过分析热成像来监测这些温度变化，有助于使用非破坏性方法对其疾病进行早期准确诊断。另一方面，指出鸟类身体的哪个部位对疾病诊断影响最大非常重要。这不仅能加快诊断过程，还能为动物病理学家确定一个重要指标。在本研究中，提出了一种智能算法，旨在对禽流感和新城疫这两种疾病在疾病传播早期进行早期诊断和分类。为此，基于热成像开发了三种不同模型，包括：原始图像、去除背景的图像以及通过YOLO-v8模型将鸡的头部和腿部分离的图像。然后，使用支持向量机（SVM）分类器在所有三种模型中评估从热成像中提取的特征，包括纹理和颜色。此外，通过Relief特征选择算法将热成像中对禽流感和新城疫这两种疾病诊断最重要且有效的特征介绍给其他研究人员。禽流感的原始图像、无背景图像以及鸡头部和腿部图像的分类准确率分别为75.89%、83.93%和92.48%，新城疫的分别为83.04%、91.52%和94.20%。为疾病早期诊断开发的模型显示，在疾病感染8小时后能够以超过90%的准确率诊断这两种疾病。结果表明，在禽类疾病诊断中，与纹理相关的特征的贡献大于从热成像中提取的其他特征。此外，通过YOLO-v8算法关注头部和脚部区域将提高分类准确率，从而能够在疾病的实时早期阶段进行更准确的诊断。
