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

Jiaqi Liu, Yanxin Hu, Qianfu Su, Jianwei Guo, Zhiyu Chen and Gang Liu ()
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Jiaqi Liu: School of Computer Science and Engineering, Changchun University of Technology, Changchun 130102, China
Yanxin Hu: School of Computer Science and Engineering, Changchun University of Technology, Changchun 130102, China
Qianfu Su: Institute of Plant Protection, Jilin Academy of Agricultural Sciences (Northeast Agricultural Research Center of China), Changchun 130033, China
Jianwei Guo: School of Computer Science and Engineering, Changchun University of Technology, Changchun 130102, China
Zhiyu Chen: School of Computer Science and Engineering, Changchun University of Technology, Changchun 130102, China
Gang Liu: School of Computer Science and Engineering, Changchun University of Technology, Changchun 130102, China

Agriculture, 2024, vol. 14, issue 7, 1-23

Abstract: 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.

Keywords: maize disease detection; semi-supervised learning; object detection (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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