AUTHOR=Fu Rui , Wang Xuewei , Wang Shiyu , Sun Hao TITLE=PMJDM: a multi-task joint detection model for plant disease identification JOURNAL=Frontiers in Plant Science VOLUME=Volume 16 - 2025 YEAR=2025 URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1599671 DOI=10.3389/fpls.2025.1599671 ISSN=1664-462X ABSTRACT=IntroductionPlant disease detection is critical for ensuring agricultural productivity, yet traditional methods often suffer from inefficiencies and inaccuracies due to manual processes and limited adaptability.MethodsThis 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.ResultsEvaluated 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.DiscussionThe superior performance of PMJDM is driven by multi-task synergy and texture - enhanced region proposals, offering an efficient solution for precision agriculture.