ArticleFrontiers in plant science2025
PMJDM: a multi-task joint detection model for plant disease identification.
Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- EDDet: efficient deep-fusion and dynamic optimization for small target detection in eggplant diseases.BMC plant biology · 2025Article
- Real-time detection method for Litchi diseases and pests based on improved YOLOv5s.Frontiers in plant science · 2025Article
- LeafSightX: an explainable attention-enhanced CNN fusion model for apple leaf disease identification.Frontiers in artificial intelligence · 2025Article
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4 authors.
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Abstract
Introduction: 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. Methods: This paper presents the PlantDisease Multi-task Joint Detection Model (PMJDM), which 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 is also employed to optimize task balance and improve robustness. Results: 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. Discussion: The superior performance of PMJDM is driven by multi-task synergy and texture - enhanced region proposals, offering an efficient solution for precision agriculture.
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