华南农业大学学报2026，Vol.47Issue(3)：369-381,13.DOI:10.7671/j.issn.1001-411X.202601035
农业视觉中的低标注学习:半监督、弱监督与自监督方法综述
Learning with limited annotations in agricultural vision:A review of semi-supervised,weakly supervised and self-supervised methods
摘要
Abstract
Traditional agricultural production systems exhibit evident limitations in production efficiency,resource utilization and environmental sustainability,and are undergoing a gradual transition toward informatization and intelligent modernization.As a core enabling technology of smart agriculture,agricultural vision plays a crucial role in key applications such as crop production monitoring as well as livestock and poultry breeding management,and has significant practical value for improving agricultural productivity.However,most existing vision models rely on large-scale labeled datasets.In agricultural scenarios,complex environments and highly variable data acquisition conditions lead to high annotation costs and long data preparation cycles,which substantially limit large-scale deployment.This paper focuses on three representative learning paradigms with limited annotations in agricultural vision,namely semi-supervised,weakly supervised and self-supervised learning.Their fundamental principles and commonly adopted frameworks are reviewed.The performance characteristics and applicability of these three learning paradigms are summarized in the context of typical agricultural vision tasks.Key challenges,including limited cross-domain generalization and interference from noisy annotations,are further analyzed.Future research directions are discussed,such as the construction of datasets and evaluation benchmarks,the development of agriculture-specific pre-trained models,and active learning-driven low-cost iterative strategies,providing references for the advancement and application of agricultural vision technologies.关键词
农业视觉/智慧农业/低标注学习/作物监测/畜牧监测/深度学习Key words
Agricultural vision/Smart agriculture/Learning with limited annotations/Crop monitoring/Livestock monitoring/Deep learning分类
信息技术与安全科学引用本文复制引用
肖德琴,刘倩,潘茜怡,黄吉,谭祖杰..农业视觉中的低标注学习:半监督、弱监督与自监督方法综述[J].华南农业大学学报,2026,47(3):369-381,13.基金项目
广东省现代农业产业智慧农业共性关键技术创新团队(2024CXTD28) （2024CXTD28）
国家现代农业产业技术体系建设专项(CARS-42-13) （CARS-42-13）
