Surveillance to Detect and Classifying of Chicken Poultry Diseases from Fecal Images Using CNN

The growing demand for livestock products among people exerts pressure on the agriculture sector, especially in chicken poultry industry, to produce more. Expanded poultry farming can result in elevated spread of various kind of infectious diseases in chickens, that can cause extensive chicken mortality and large financial damages. In order to identify various diseases in chickens, a variety of techniques are utilized, such as measuring the number of oocytes in the feces or intestinal tract, looking for viruses, and performing polymerase chain reaction (PCR) tests, which call for various diagnoses. Farmers lose a lot of domesticated birds due to delayed diagnosis or a lack of reliable specialists. By analyzing images of chicken droppings, utilizing machine learning as well as artificial intelligence techniques based on computer screening and image analysis, it is possible to quickly identify the most prevalent illnesses afflicting hens. In this paper, through the classification of healthy and harmful fecal pictures various deep convolutional neural network (CNN) model was discussed to identify poultry diseases.