Newcastle Disease Prediction in Chickens Using Image-Based Deep Learning

Description

Newcastle Disease is a highly contagious viral disease that poses a significant threat to poultry health and productivity, potentially causing substantial economic losses for farmers. Early detection is essential to minimize disease transmission and support effective poultry farm management. This project aims to develop an artificial intelligence (AI)-driven system for the early prediction of Newcastle Disease in chickens by integrating deep learning, computer vision, and multimodal data analysis. The proposed system analyzes three complementary sources of information: chicken head and body images, respiratory sounds, and feeding behavior. Convolutional Neural Networks (CNNs) with transfer learning will be employed to identify visual symptoms, while deep learning models will analyze respiratory abnormalities, such as coughing and gasping. Additionally, computer vision techniques will monitor feeding activities to identify abnormal behavioral patterns. A multimodal fusion approach will combine these features to classify chickens as healthy or potentially infected. The system will incorporate a user-friendly web interface with Arabic language support, enabling farmers to upload data, receive predictions, and obtain early warnings. Ultimately, the project aims to improve disease monitoring, support timely veterinary intervention, reduce poultry losses, and promote sustainable poultry farming practices.