Mathematical Formulation of Deep Learning Model for Poultry Disease Classification using EfficientNet- B3 CNN Model

Abstract

The agricultural industry, and particularly the poultry farming industry, is under pressure to increase output levels due to the growing human demand for animal products. There can be substantial economic losses and widespread chicken mortality as a consequence of an in-crease in infectious disease transmission caused by expanded poultry production. When compared to conventional ways for poultry disease identification, manual methods are time-consuming, labour-intensive, and error-prone. Also, experts' knowledge is usually necessary for making sense of the results. These restrictions raise the danger of the disease spreading across the flock and make it more difficult to diagnose diseases in a timely manner, both of which can have devastating effects. For a long time now, farmers have relied on specialists to identify and diagnose chicken illnesses. Consequently, many domesticated birds end up in the hands of farmers who suffer from either unreliable specialists or delayed diagnosis. The most prevalent chicken illnesses may be quickly recognised from pictures of chicken drop-pings using the techniques that are already accessible from artificial intelligence (AI) based on computer vision.
In this paper, a deep learning approach offered that uses a pre-trained Convolution Neural Networks (CNN) model to determine which of the three categories best describes chicken excrement and offer a method for identifying and categorising poultry illnesses. The Effi-cientNet-B3 model was utilised in the development of the system. Coccidiosis, Salmonella, New Castle Disease and Healthy were the four health problems that were classified using the segmented picture by the deep learning model. The models were trained using standard benchmark database images of excrement from chickens. The outcomes of experiment demonstrate that the proposed method for identifying and categorizing chicken illnesses may accurately identify three prevalent poultry diseases. Consequently, this approach has the potential to be an invaluable resource for farm veterinarians and poultry producers.