Development and design of an intelligent system for poultry diseases prediction

Abstract

The poultry industry faces significant challenges in maintaining the health and welfare of birds, with the emergence of new and complex diseases. Predicting and diagnosing these diseases is crucial for effective management and control. In recent years, there has been a growing interest in the development of intelligent systems that can aid in predicting the occurrence of poultry diseases using sound classification. This study presents the development and design of an intelligent system for predicting poultry diseases based solely on sound classification. The system analyzes audio recordings of poultry vocalizations to detect abnormal sounds that may indicate the presence of disease. The system is also designed to provide recommendations on the appropriate control measures to be taken in the event of an outbreak. The proposed system uses a combination of signal processing, feature extraction, and classification algorithms to predict the likelihood of a disease outbreak based on sound recordings. The system's performance was evaluated using data collected from a poultry farm, and the results show that the system achieved high accuracy in predicting the occurrence of diseases using sound classification. Overall, the development of an intelligent system for predicting poultry diseases based on sound classification can significantly improve disease management and control in the poultry industry. The system can assist in early detection of diseases, reducing the risk of transmission and minimizing the impact on poultry production