ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1599671
PMJDM: A Multi-Task Joint Detection Model for Plant Disease Identification
Provisionally accepted
- Weifang University of Science and Technology, Weifang, China
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Plant disease detection is critical for ensuring agricultural productivity, yet traditional methods often suffer from inefficiencies and inaccuracies due to manual processes and limited adaptability. This paper presents the PlantDisease Multi-task Joint Detection Model (PMJDM) integrates an enhanced ConvNeXt-based shared feature extraction, a texture-augmented N-RPN module with HOG/LBP metrics, multi-task branches for simultaneous plant species classification and disease detection, and CRF-based post-processing for spatial consistency. A dynamic weight adjustment mechanism optimizes task balance, improving robustness. Evaluated on a 26,073-image dataset, PMJDM achieves 71.84% precision, 61.96% recall, and 61.83% mAP50, surpassing Faster-RCNN (51.49% mAP50) and YOLOv10x (59.52% mAP50) by 10.34% and 2.31%, respectively. This superior performance, driven by multi-task synergy and texture-enhanced region proposals, offers an efficient solution for precision agriculture.
Keywords: Plant disease detection, Multi-task learning, Candidate region generation, Conditional random fields, Dynamic weight adjustment
Received: 25 Mar 2025; Accepted: 25 Apr 2025.
Copyright: © 2025 Fu, Wang, Wang and Sun. 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: Hao Sun, Weifang University of Science and Technology, Weifang, China
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