Animal phenotyping recognition is a pivotal component of precision livestock management, holding significant importance for intelligent farming practices and animal welfare assurance. In recent years, with the rapid advancement of deep learning technologies, the YOLO algorithm—as the pioneering single-stage detection framework—has revolutionized the field of object detection through its efficient and rapid approach and has been widely applied across various agricultural domains. This review focuses on animal phenotyping as the research target structured around four key aspects: (1) the evolution of YOLO algorithms, (2) datasets and preprocessing methodologies, (3) application domains of YOLO algorithms, and (4) future directions. This paper aims to offer readers fresh perspectives and insights into animal phenotyping research.
Read full abstract- All Solutions
Editage
One platform for all researcher needs
Paperpal
AI-powered academic writing assistant
R Discovery
Your #1 AI companion for literature search
Mind the Graph
AI tool for graphics, illustrations, and artwork
Unlock unlimited use of all AI tools with the Editage Plus membership.
Explore Editage Plus - Support
Overview
1052 Articles
Published in last 50 years
Articles published on Farm Animal Welfare
Authors
Select Authors
Journals
Select Journals
Duration
Select Duration
940 Search results
Sort by Recency