Abstract
Urban street trees, particularly plane trees in Shanghai, are susceptible to pests and diseases like holes, decay, termite, and longicorn. Traditional manual inspections are labor-intensive and error-prone. This study introduces an enhanced YOLOv8-based detection framework to address multi-scale variability in pest and disease datasets. Key improvements include integrating the Selective Kernel Attention (SKA) mechanism for multi-scale feature enhancement, augmenting the Spatial Pyramid Pooling Fast (SPPF) module with average pooling to preserve subtle details, and employing the WIoUv3 loss function to improve localization accuracy. Trained on 3,983 annotated samples from Shanghai, the model achieved a 3.8% increase in mean Average Precision at a 50% Intersection over Union threshold (mAP50) and a significant reduction in missed detections compared to the baseline YOLOv8. Robustness was validated through experiments with varied parameters, repeated training sessions, and stratified sampling addressing class imbalance. This research demonstrates the model's potential for intelligent, automated monitoring of urban tree health.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 2
Claim · model-written summary
The enhanced YOLOv8 detector increased mAP50 by 3.8% compared with baseline YOLOv8 for plane tree trunk pests and diseases.
- Interactor
- termitee0pest
- Second interactor
- longicorne0pest
- Outcome
- mean Average Precision at a 50% Intersection over Union threshold (mAP50)e1
- Direction
- increasee1
- Value
- 3.8%e1
- Comparator
- baseline YOLOv8e1
- Setting
- Shanghaie0
PlatanusOrganisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Urban street trees, particularly plane trees in Shanghai, are susceptible to pests and diseases like holes, decay, termite, and longicorn”
e1“the model achieved a 3.8% increase in mean Average Precision at a 50% Intersection over Union threshold (mAP50) and a significant reduction in missed detections compared to the baseline YOLOv8”
Claim · model-written summary
The enhanced YOLOv8 detector significantly reduced missed detections compared with baseline YOLOv8 for plane tree trunk pests and diseases.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Urban street trees, particularly plane trees in Shanghai, are susceptible to pests and diseases like holes, decay, termite, and longicorn”
e1“the model achieved a 3.8% increase in mean Average Precision at a 50% Intersection over Union threshold (mAP50) and a significant reduction in missed detections compared to the baseline YOLOv8”