인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
논문을 무제한 열람 이용할 수 있어요.
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
In this work, we present a novel approach for the early detection and diagnosis of skin diseases in farm animals, a major concern that can lead to reduced productivity, decreased animal welfare, and economic losses. Using Internet of Things (IoT) and MobileNetV2, we have developed a system that is built using Raspberry Pi for the gateway and low-power ESP 32 microcontrollers for sensor attachment. This system consists of sensors placed on the animals" bodies, including an electrocardiogram (ECG) sensor and a DS18B20 temperature sensor, which continuously monitor the animals" vital signs and skin temperature. The collected data is transmitted to a central server where it is processed using MobileNetV2, a deep learning model trained to recognize three common skin diseases in farm animals: Dermatophilosis, Dermatophycosis, and Papillomatosis. The results of this processing are then made available to animal owners and farmers through a mobile app. Our results show that the proposed system can accurately detect and diagnose skin diseases in farm animals with a high degree of recall (0.96), precision (0.96), and f1 score (0.96). The use of IoT and machine learning allows for realtime monitoring and early detection of skin diseases, which can significantly reduce the spread of infection and improve the overall health and welfare of farm animals. In addition, the system is intended to support veterinarians in assessing the health status of farm animals. Overall, this work demonstrates the potential of using IoT and machine learning for the early detection and diagnosis of skin diseases in farm animals and highlights the importance of continuous monitoring and proactive management in maintaining the health and welfare of these animals.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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최근 본 자료 전체보기
UCI(KEPA) : I410-ECN-0101-2023-004-000373797
