Detection and Classification of Disease in Poultry farm

Abstract

Poultry disease outbreaks have become more common in recent years, wreaking havoc on the poultry industry. They have not only cost farmers a lot of money, but they have also put people's health in jeopardy. As a result, chicken illness has become a major concern for poultry producers and the country as a whole. The emergence of poultry illnesses can frequently pose a major hazard to human health. Despite the large volume and intensity of chicken husbandry, poultry disease surveillance is still reliant on manual observation. The disease called Newcastle disease (NCD) causes more death of broilers in poultry. The virulent Newcastle disease virus infects domestic chickens and other bird species, causing NCD (NDV). It's a worldwide condition that usually manifests as an acute respiratory infection, but it can also express as sadness, nervousness, or diarrhoea. We construct a bespoke model based on the YOLO v3 algorithm to identify illnesses in broilers in poultry in this project. The YOLO weight file has been developed by using Darknet framework and deployment of the model is done by using Tkinter model in python. The model performs well when compared with other deep learning algorithms.