인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
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.
피인용 0
초록·키워드
Timely detection of farrowing in sows is important for effective farm management and animal welfare. However, existing farrowing monitoring approaches are largely contact-based, which limits their practical applicability in farm environments due to constraints such as cumulative equipment costs and animal stress. To address these limitations, this study developed a deep learning-based non-contact farrowing classification system for sows. The proposed method employed precise region of interest (ROI) cropping based on the Segment Anything Model (SAM) to reduce background interference and consistently include farrowing-related regions. In addition, multiple-instance learning was integrated into a Convolutional Neural Network (CNN)-based classification framework to better aggregate region-wise discriminative cues. Experimental results showed that the proposed method achieved the best overall performance among the compared models, with 85.47% recall and 85.68% F1-score. Compared with the original full-image input, it improved recall by 3.59 percentage points and F1-score by 3.71 percentage points. These results indicate that precise ROI cropping and multiple-instance learning jointly improve farrowing classification performance by reducing background interference and better aggregating region-wise discriminative cues.
본문·목차
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