智能化农业装备学报（中英文）2026，Vol.7Issue(3)：109-120,12.DOI:10.12398/j.issn.2096-7217.2026.03.010
基于半监督一致性学习的复杂场景枸杞虫害检测
Wolfberry pest detection in complex scenarios based on semi-supervised consistency learning
摘要
Abstract
In the context of intelligent agriculture,accurate identification of wolfberry pests is important for ensuring wolfberry yield and improving field management efficiency.However,due to small target sizes,dense distributions,complex backgrounds,and high annotation costs,existing fully supervised object detection methods suffer from limited generalization and a strong dependence on labeled samples in wolfberry pest detection.To address this challenge,this study proposes DT-WPSSD(Dual-Teacher Wolfberry Pest Semi-Supervised Detection),a semi-supervised detection framework for five wolfberry pest categories:thrips,flea beetles,aphids,leaf beetles,and psyllids.Built on YOLOv8,DT-WPSSD establishes a semi-supervised framework with a dual-teacher branch architecture.The network is trained on available labeled samples and generates pseudo-labels for unlabeled samples;these pseudo-labels are then categorized into three reliability levels(unreliable,uncertain,and reliable)to suppress error propagation from low-quality pseudo-labels.In addition,a cross-supervision mechanism is constructed to constrain the consistency of predictions from different teacher models,and a pseudo-label fusion strategy is designed to improve the quality and stability of pseudo-labels.Meanwhile,a multi-scale wolfberry pest dataset(MSPD)under complex scenarios was collected and reconstructed,and pest samples at large,medium,and small scales as well as in dense scenes were systematically analyzed.Finally,to address the increasingly complex model structure and rising computational cost,model pruning experiments were conducted.DT-WPSSD achieved mAP@0.5 scores of 88.2%and 57.1%on the MSPD and IP102 datasets,respectively,significantly outperforming semi-supervised baseline models.After pruning,the parameters of DT-WPSSD were reduced by 6 M and floating-point operations by 7.1×109.These results demonstrate that DT-WPSSD improves detection stability and small-target recognition capability in complex scenarios.关键词
目标检测/半监督学习/一致性学习/模型剪枝/枸杞虫害/复杂场景Key words
object detection/semi-supervised learning/consistency learning/model pruning/wolfberry pests/complex scenes分类
农业科技引用本文复制引用
陈曦,刘建平,张建华,王健..基于半监督一致性学习的复杂场景枸杞虫害检测[J].智能化农业装备学报（中英文）,2026,7(3):109-120,12.基金项目
宁夏回族自治区全职引进高层次人才(2024BEH04130) （2024BEH04130）
国家自然科学基金项目(32460444) （32460444）
宁夏自然科学基金项目(2025AAC050001) （2025AAC050001）
宁夏回族自治区青年科技托举人才(22225000502) Ningxia Hui Autonomous Region Full-Time Introduced High-Level Talent Program(2024BEH04130) （22225000502）
National Natural Science Foundation of China(32460444) （32460444）
Ningxia Natural Science Foundation Project(2025AAC050001) （2025AAC050001）
Ningxia Hui Autonomous Region Youth Science and Technology Talent Support Program(22225000502) （22225000502）
