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Semi-Supervised One-Stage Object Detection for Maize Leaf Disease

文献类型： 外文期刊

作者： Liu, Jiaqi 1 ; Hu, Yanxin 1 ; Su, Qianfu 2 ; Guo, Jianwei 1 ; Chen, Zhiyu 1 ; Liu, Gang 1 ;

作者机构： 1.Changchun Univ Technol, Sch Comp Sci & Engn, Changchun 130102, Peoples R China

2.Jilin Acad Agr Sci, Inst Plant Protect, Northeast Agr Res Ctr China, Changchun 130033, Peoples R China

3.Jilin Prov Data Serv Ind Publ Technol Res Ctr, Changchun 130102, Peoples R China

关键词： maize disease detection; semi-supervised learning; object detection

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.3； 五年影响因子：3.5 ）

ISSN：

年卷期： 2024 年 14 卷 7 期

页码：

收录情况： SCI

摘要： Maize is one of the most important crops globally, and accurate diagnosis of leaf diseases is crucial for ensuring increased yields. Despite the continuous progress in computer vision technology, detecting maize leaf diseases based on deep learning still relies on a large amount of manually labeled data, and the labeling process is time-consuming and labor-intensive. Moreover, the detectors currently used for identifying maize leaf diseases have relatively low accuracy in complex experimental fields. Therefore, the proposed Agronomic Teacher, an object detection algorithm that utilizes limited labeled and abundant unlabeled data, is applied to maize leaf disease recognition. In this work, a semi-supervised object detection framework is built based on a single-stage detector, integrating the Weighted Average Pseudo-labeling Assignment (WAP) strategy and AgroYOLO detector combining Agro-Backbone network with Agro-Neck network. The WAP strategy uses weight adjustments to set objectness and classification scores as evaluation criteria for pseudo-labels reliability assignment. Agro-Backbone network accurately extracts features of maize leaf diseases and obtains richer semantic information. Agro-Neck network enhances feature fusion by utilizing multi-layer features for collaborative combinations. The effectiveness of the proposed method is validated on the MaizeData and PascalVOC datasets at different annotation ratios. Compared to the baseline model, Agronomic Teacher leverages abundant unlabeled data to achieve a 6.5% increase in mAP (0.5) on the 30% labeled MaizeData. On the 30% labeled PascalVOC dataset, the mAP (0.5) improved by 8.2%, demonstrating the method's potential for generalization.

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