Abstract
Poultry production is essential worldwide due to its role in supplying meat and eggs, which are rich in protein and vital nutrients for human diets. quick spread of sickness among the chicken, which may be uncontrollable by humans, causes a significant loss in the poultry even if farmers can save money on it since it requires little in the way of resources to feed the birds. Recently many technologies have been developed to detect chicken disease, but the technologies faced certain issues such as increased time consumption, inefficient detection, and so on. To defeat the mentioned challenges, a proposed method named grey wolf optimized Deep Convolutional Neural Network (GWO-Deep CNN) is designed to enrich the performance of research by detecting the disease accurately and further helps veterinarians to diagnose the disease properly, which reduces the death rate among the chickens in the poultry. The Deep CNN is utilized effectively to detect the disease accurately and classify the detected disease. Performance metrics utilized to analyze the performance of the GWO-Deep CNN are accuracy, sensitivity, and specificity, which attain 0.952, 0.962, and 0.940 respectively.