스마트 축사내 상황인지 자율이동형 환경센서 개발 및 가축행동 분석에 관한 연구

Abstract

This study aims to develop a system that predicts the health status of cattle based on behavior patterns and environmental data within a smart barn using an autonomous driving system. Maintaining a unique ID for each cow using only a camera, without external devices (such as RFID tags), is essential. This enables the tracking of behavior patterns such as standing, sitting, and lying for each cow over time. Additionally, environmental data such as temperature and humidity are integrated to comprehensively assess the cows' health conditions. To achieve this, we propose a unique ID retention algorithm that combines object detection using YOLO, tracking with Deep SORT, and re-identification (ReID). Experimental results show that the YOLO + Deep SORT + ReID algorithm delivers the best performance in maintaining unique IDs, and the LSTM-based behavior analysis model demonstrates high accuracy in predicting behavior patterns. This system can serve as an effective tool for real-time prediction of livestock health conditions, such as disease or stress, through comprehensive analysis of environmental data and behavior patterns inside the barn.