Object Detection Approach for Pure and Cross Chicken Breed Identification

Abstract

The proposed system aims to identify different types of purebred and crossbred chicken breeds across ten categories. Traditional methods such as visual inspection are often subjective and inaccurate, making breed identification challenging. To address this, image processing and deep learning techniques were employed, with the YOLOv5 object detection algorithm trained on a custom data set of 1,310 images. The model achieved strong results, with an overall precision of 83.5%, recall of 81.8%, and mAP@0.5 of 89.3%. The Class-wise evaluation showed particularly high performance for the Brahma and Leghorn breeds. Based on these outcomes, a mobile application was designed to provide farmers with a fast, reliable, and cost-effective tool for breed identification. In addition to classification, the application provides detailed information on breed characteristics and commercial value, helping small-scale farmers improve productivity, efficiency, and animal welfare