Image-based Detection and Classification of Poultry Diseases from Chicken Droppings in Open House Poultry Farms

Monitoring chicken health is essential for maintaining the production efficiency of poultry farms and meeting the demand for poultry products. Previous studies have explored various methods, including utilizing sound, behaviour, and the shape of the chickens, as well as the conditions of their droppings, to assess chicken health. In this research, we monitor chicken droppings as a reliable indicator of chicken health. We develop an automated system for detecting chicken droppings and identifying health conditions, specifically in open house poultry farms in Malaysia. Open poultry houses are the most common design in Malaysia due to their lower construction and maintenance costs, a more natural environment for the chickens, and greater space to roam. However, the design of open poultry houses, which utilizes evenly gapped wood slat flooring, compounds the problem of automatically distinguishing new droppings from dirty flooring. In our work, data consisting of chicken dropping images from a poultry farm in Malaysia were collected for analysis. We used the YOLOv5n algorithm for detecting chicken droppings and distinguishing between healthy and sick chickens based on observable features such as the colour and shape of their droppings. Our proposed architecture, which used the YOLOv5n algorithm, can accurately detect chicken droppings and classify them into three health classes (coccidiosis, healthy, and other unhealthy), with an accuracy rate of up to 94.9%. By leveraging advanced computer vision techniques, poultry farmers can benefit from timely and accurate health assessments, leading to improved productivity and animal welfare in open house poultry farming systems.