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ORIGINAL RESEARCH article

Front. Plant Sci.

Sec. Sustainable and Intelligent Phytoprotection

Volume 16 - 2025 | doi: 10.3389/fpls.2025.1642453

This article is part of the Research TopicPrecision Information Identification and Integrated Control: Pest Identification, Crop Health Monitoring, and Field ManagementView all 24 articles

DSA-Net: A Lightweight and Efficient Deep Learning-Based Model for Pea Leaf Disease Identification

Provisionally accepted
Laixiang  XuLaixiang Xu1Yiru  DuanYiru Duan1Zhaopeng  CaiZhaopeng Cai1Wenwen  HuangWenwen Huang2Fengyan  ZhaiFengyan Zhai2Junmin  ZhaoJunmin Zhao1*
  • 1Henan University of Urban Construction, Pingdingshan, China
  • 2Henan Institute of Science and Technology, Xinxiang, China

The final, formatted version of the article will be published soon.

Pea is a nutrient-dense, functionally diversified vegetable. However, its leaf diseases have a direct impact on yield and quality. Most approaches for identifying pea leaf diseases exhibit low feature extraction efficiency, significant environmental sensitivity, and limited large-scale applications, making it impossible to meet the expectations of modern agriculture for accuracy, realtime processing, and low cost. Therefore, we propose a deep learning model for pea leaf disease identification based on an improved MobileNet-V3_small, deformable convolution strategy, selfattention, and additive attention mechanisms (DSA-Net). First, a deformable convolution is added to MobileNet-V3-small to increase the modeling skills for geometric changes in disease features.Second, a self-attention mechanism is integrated to improve the ability to recognize global features of complex diseases. Finally, an additive attention strategy to enhance the feature channel and spatial position response relationship in edge-blurred lesion areas. The experimental pea leaf data set consists of 7915 samples divided into five categories. It includes one healthy leaf and four diseases: brown spot, leaf miner, powdery mildew, and root rot. The experimental results indicate that the suggested DSA-Net has an average recognition accuracy of 99.12%. It has a parameter size of 1.48M.The proposed approach will help with future edge device deployments. The current proposed technique considerably enhances the diagnostic accuracy of pea leaf diseases and has significant promotion and application potential in agriculture.

Keywords: Leaf disease, Pea leaf, deep learning, MobileNet-V3_small, attention mechanism

Received: 06 Jun 2025; Accepted: 01 Aug 2025.

Copyright: © 2025 Xu, Duan, Cai, Huang, Zhai and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Junmin Zhao, Henan University of Urban Construction, Pingdingshan, China

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