Deteksi Ayam yang Terindikasi Penyakit Berbasis Citra Digital Menggunakan Metode YOLOv11

Abstract

The high mortality rate of chickens on farms due to late detection of diseases is a serious problem that has an impact on the productivity and economy of farmers. This condition encourages the need for an automated system to detect chickens that are indicated as disease early. This study aims to develop a digital image-based chicken identification system that is indicated as disease using the You Only Look Once version 11, YOLOv11 method, which is capable of object detection. This system works by analyzing the visual characteristics of chickens, such as body posture, and other physical conditions, to classify chickens into healthy or sick categories. The model training process is carried out with a dataset of healthy and sick chicken images that have been labeled. It is hoped that this system can increase the speed and accuracy in detecting sick chickens, thereby helping farmers take preventive measures earlier, reducing mortality rates, and improving livestock capabilities.