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Article 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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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12137281 | PMC |
| http://dx.doi.org/10.3389/fpls.2025.1599671 | DOI Listing |
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