Abstract:
Human demand for animal products is increasing, forcing the agricultural industry, particularly poultry farming,
to increase the quantity of its output. Increased poultry farming can lead to increased transmission of infectious
diseases, resulting in widespread poultry death and significant economic losses. Traditional techniques for
detecting diseases in poultry involve manual methods that are labor-intensive, time-consuming, and error-prone.
Furthermore, the interpretation of the results often requires the expertise of trained professionals. These limi tations can impede timely disease detection and increase the risk of the disease spreading throughout the flock,
which can have severe consequences. This paper presents a detection and classification system for poultry dis eases. The system was developed using two core algorithms: YOLO-V3 object detection algorithm and ResNet50
image classification model. YOLO-V3 was used to segment region of interest (ROI) from faecal images while
ResNet50 was used for classification of the segmented image into four health conditions: Health, Coccidiosis,
Salmonella, and New Castle Disease. The models were trained on 10,500 chicken faecal images collected from
Zenodo open database. Oversampling and image augmentation techniques were applied to the dataset to handle
class imbalance prior to training the ResNet50 model. The YOLO-V3 object detection model, implemented in
Darknet, achieved a mean average precision of 87.48% for detecting regions of interest (ROI), while the
ResNet50 image model demonstrated a classification accuracy of 98.7%. Based on our experimental findings, the
proposed chicken disease detection and classification system exhibits the ability to accurately identify three
prevalent poultry diseases. Therefore, this system can prove to be a valuable tool for assisting poultry farmers
and veterinarians in farm settings.
