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:

动植物表型是动植物特征与性状的定量描述，表型特征的精准计算与分析是推进数字农业发展的重要基础。得益于深度学习技术的迅猛发展，以YOLO系列算法为代表的计算机视觉模型在动植物表型分析任务中展现出了优良性能和巨大潜力。以家畜类、家禽类、作物类、果蔬类等动植物为对象，分别从目标检测、关键点检测、目标分割3方面概述了YOLO系列算法应用研究进展。最后指出YOLO系列算法未来发展趋势，包括轻量化架构设计、小目标精准检测、弱监督学习、复杂场景部署、大模型目标检测等。

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.