# Research Progress in the Early Warning of Chicken Diseases by Monitoring Clinical Symptoms
## 2 Early Diseases Detection through Physiological Characteristics
### Abnormal Faces  
Digestive system disease is one of the most widespread diseases in chicken farming, which seriously affects production and animal welfare [64]. When the gastrointestinal tract of chickens is infected by bacteria or viruses, there will be different degrees of pathological changes according to the severity of infection, which will be manifested as abnormal feces in clinical practice. Figure 4 shows the normal and abnormal feces from typical digestive diseases in chickens. Therefore, feces can be used as the basis for the early warning of chicken diseases. Table 1 lists the common diseases causing diarrhea in chickens and the corresponding fecal external characteristics. The traditional method of fecal identification relying on experienced veterinarians is feasible to some extent, but it is time consuming, laborious and prone to misdiagnosis. So, researchers began to use machine learning algorithms to address agricultural tasks. SVM is a kind of generalized linear classifier that classifies binary data by supervised learning, and its decision boundary is the maximum margin hyperplane for the positive and negative classes [65; 66]. [PERSON] et al. [29] were the first to combine the external characteristics of chicken feces with SVM for chicken disease detection. They used the gray-level co-occurrence matrix, a widely known texture descriptor consisting of the extraction of some statistical measurements about the co-existence of different grey levels from the image [67] to extract 19 texture features of feces, and took the texture feature set as the input of SVM to classify the feces of healthy and diseased chickens. The results showed that the highest \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name of Disease** & **Vulnerable Time** & **Degree of Hazard \({}^{*}\)** & **Characteristics of Feces** \\ \hline Avian influenza & All ages & \(\star\star\star\star\star\) & Yellow-brown and watery \\ Newcastle disease & All ages & \(\star\star\star\star\star\) & Yellow-green and watery \\ Infectious burs disease & 3–6 weeks & \(\star\star\star\star\) & Time watery \\ Infectious bronchitis & 3–7 weeks & \(\star\star\star\star\) & White and watery \\ Avian cholera & Adult & \(\star\star\star\star\) & Grass-green and watery \\ Pullorum disease & 0–2 weeks & \(\star\star\star\) & White mushy \\ Coccidiosis & 4–6 weeks & \(\star\star\star\) & Brown-red thin or blood \\ Tapeworm disease & All ages & \(\star\star\) & Tapeworm eggs \\ \hline \hline \end{tabular} \({}^{*}\) The degree of hazard was represented by the number of symbol \(\star\): more symbols represent a more hazardous condition.  
\end{table} 
Table 1: Common diseases causing diarrhea in chickens and corresponding fecal characteristics. Figure 4: The normal and abnormal feces from typical digestive diseases in chickens. The feces of disease-infected chickens may have abnormal changes in color, shape, water content and contents. accuracy was 93.75%, which proved the feasibility of detecting sick chickens by feces characteristics, but only 20 samples were used in the experiment, so the results may not be representative. The classification of feces is only the first step, and how to detect abnormal feces in images that contain a lot of feces is crucial. The development of deep learning algorithms makes it possible to deal with larger number of samples. [PERSON] et al. [68] collected 10,000 images of feces of 25- to 35-day-old Ross broilers on commercial chicken farms. They manually marked them into five categories: normal, abnormal shape, abnormal color, abnormal water content, abnormal shape and water content. Their study proposed two object detection models based on CNN to detect and classify feces. The results showed that the Faster R-CNN model had better performance with 99.1% recall and 93.3% average mean precision on the test dataset. This study provides a new idea for the detection of digestive tract diseases in poultry on commercial farms.