An On-Site Feces Image Classifier System for Chicken Health Assessment: A Proof of Concept

Abstract

Highlights A mobile application embedded onto smart mobile devices was developed for on-site chicken health assessment based on fecal images. A trained deep learning image classification model was programmed into the application for classifying healthy birds or unhealthy birds infected with Coccidiosis , Salmonella , and Newcastle disease . Animal caretakers can capture fecal images on farms, upload them to the developed application on their mobile devices, and receive health assessment results during daily flock inspection. The study demonstrates a successful proof-of-concept system but requires further work for consolidating system performance. Abstract. Rapid and accurate chicken health assessment can assist producers in making timely decisions, reducing disease transmission, improving animal welfare, and decreasing economic loss. The objective of this research was to develop and evaluate a proof-of-concept mobile application system to assist caretakers in assessing chicken health during their daily flock inspections. A computer server was built to assign users with different usage credentials and receive uploaded fecal images. A dataset containing fecal images from healthy and unhealthy birds (infected with Coccidiosis, Salmonella, and Newcastle disease) was used for classification model development. The modified MobileNetV2 model with additional layers of artificial neural networks was selected after a comparative evaluation of six models. The developed model was embedded into a local server for image classification. An application was developed and deployed, allowing a user with the application on a mobile device to upload a fecal image to a website hosted on the server and receive results processed by the model. Health status is transferred back to the user and can be shared with production managers. The system achieved over 90% accuracy for identifying diseases, and the whole operational procedure took less than one second. This proof-of-concept demonstrates the feasibility of a potential framework for mobile poultry health assessment based on fecal images. However, further development is needed to expand applicability to different production systems through the collection of fecal images from various genetic lines, ages, feed components, housing backgrounds, and flooring types in the poultry industry and improve system performance. Keywords: Artificial intelligence, Coccidiosis, Newcastle disease, Salmonella, Software development.