农业机械学报2024，Vol.55Issue(11)：1-20,20.DOI:10.6041/j.issn.1000-1298.2024.11.001
YOLO算法在动植物表型研究中应用综述
Review of Applying YOLO Family Algorithms to Analyze Animal and Plant Phenotype
摘要
Abstract
Plant and animal phenotypes are quantitative descriptions of their characteristics and traits.Accurate analysis of phenotypic features is an important prerequisite for the development of digital agriculture.The traditional phenotypic analysis task heavily relies on manual identification and measurement by agricultural experts,which is labor-intensive,costly,and sensitive to subjective judgments.Also,the traditional approach can hardly process high-throughput data.Benefited by the rapid development of the deep learning technique,as one of the most representative computer vision models,the YOLO family algorithms have shown excellent performance and great potential in plant and animal phenotypic analysis tasks,including disease diagnosis,behavior quantification,biomass estimation,and so on.In this review,livestock,poultry,crops,fruits,vegetables,and other plants and animals were chosen as the research targets.The research progress of YOLO family algorithm applications was summarized from three aspects,namely,object detection,key point detection,and object segmentation.Along the same lines,some commonly used datasets for plant and animal phenotyping tasks for subsequent researchers were presented.Finally,the potential problems faced by current researching and the future development trend of YOLO family algorithms were highlighted,including lightweight architecture design,accurate detection of small targets,weakly supervised learning,complex scene deployment,and large model for target detection.The research aimed at providing summarization and guidance for plant and animal phenotypic analysis based on YOLO family algorithms and promoting the further development of digital agriculture.关键词
YOLO/动植物表型/目标检测/关键点检测/目标分割/轻量化Key words
YOLO/animal and plant phenotype/object detection/key-point detection/object segmentation/lightweight分类
信息技术与安全科学引用本文复制引用
翟肇裕,张梓涵,徐焕良,王海清,陈曦,杨陈敏..YOLO算法在动植物表型研究中应用综述[J].农业机械学报,2024,55(11):1-20,20.基金项目
国家自然科学基金项目(32401697)、江苏省自然科学基金项目(BK20231004)和江苏省科技计划专项资金项目(BE2023369) （32401697）
