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Mutual learning with memory for semi-supervised pest detection

文献类型：期刊论文

作者Zhou, Jiale1,3; Huang, He1,2; Sun, Youqiang1; Chu, Jiqing1; Zhang, Wei1,3; Qu, Fang1,3; Yang, Huamin1,3
刊名FRONTIERS IN PLANT SCIENCE
出版日期2024-06-17
卷号15
关键词semi-supervised pest detection mutual learning memory fusion Spatial-aware Multi-Resolution Feature Extraction cascade RPN
ISSN号1664-462X
DOI10.3389/fpls.2024.1369696
通讯作者Huang, He(hhuang@iim.ac.cn) ; Sun, Youqiang(yqsun@iim.ac.cn)
英文摘要Effectively monitoring pest-infested areas by computer vision is essential in precision agriculture in order to minimize yield losses and create early scientific preventative solutions. However, the scale variation, complex background, and dense distribution of pests bring challenges to accurate detection when utilizing vision technology. Simultaneously, supervised learning-based object detection heavily depends on abundant labeled data, which poses practical difficulties. To overcome these obstacles, in this paper, we put forward innovative semi-supervised pest detection, PestTeacher. The framework effectively mitigates the issues of confirmation bias and instability among detection results across different iterations. To address the issue of leakage caused by the weak features of pests, we propose the Spatial-aware Multi-Resolution Feature Extraction (SMFE) module. Furthermore, we introduce a Region Proposal Network (RPN) module with a cascading architecture. This module is specifically designed to generate higher-quality anchors, which are crucial for accurate object detection. We evaluated the performance of our method on two datasets: the corn borer dataset and the Pest24 dataset. The corn borer dataset encompasses data from various corn growth cycles, while the Pest24 dataset is a large-scale, multi-pest image dataset consisting of 24 classes and 25k images. Experimental results demonstrate that the enhanced model achieves approximately 80% effectiveness with only 20% of the training set supervised in both the corn borer dataset and Pest24 dataset. Compared to the baseline model SoftTeacher, our model improves mAP @0.5 (mean Average Precision) at 7.3 compared to that of SoftTeacher at 4.6. This method offers theoretical research and technical references for automated pest identification and management.
资助项目National Key Research and Development Program of China[2021YFD200060102] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA28120402] ; HFIPS Director's Fund[2023YZGH04]
WOS研究方向Plant Sciences
语种英语
WOS记录号WOS:001258024500001
出版者FRONTIERS MEDIA SA
资助机构National Key Research and Development Program of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; HFIPS Director's Fund
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/136720]  
专题中国科学院合肥物质科学研究院
通讯作者Huang, He; Sun, Youqiang
作者单位1.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei, Peoples R China
2.Anhui Zhongke Intelligent Sense Ind Technol Res In, Technol Res & Dev Ctr, Wuhu, Peoples R China
3.USTC, Sci Isl Branch, Grad Sch, Hefei, Peoples R China
推荐引用方式
GB/T 7714		 Zhou, Jiale,Huang, He,Sun, Youqiang,et al. Mutual learning with memory for semi-supervised pest detection[J]. FRONTIERS IN PLANT SCIENCE,2024,15.
APA Zhou, Jiale.,Huang, He.,Sun, Youqiang.,Chu, Jiqing.,Zhang, Wei.,...&Yang, Huamin.(2024).Mutual learning with memory for semi-supervised pest detection.FRONTIERS IN PLANT SCIENCE,15.
MLA Zhou, Jiale,et al."Mutual learning with memory for semi-supervised pest detection".FRONTIERS IN PLANT SCIENCE 15(2024).

入库方式： OAI收割

来源：合肥物质科学研究院

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