PGCNet: a Transformer-CNN hybrid segmentation model for pine wilt disease identification | JoVE Visualize

Area of Science:

Forestry
Remote Sensing
Computer Vision

Background:

Pine wilt disease is a severe threat to forest ecosystems due to its rapid spread and high mortality.
Current UAV-based identification methods using single CNN or Transformer architectures have limitations in capturing global context and local details.
Existing feature fusion strategies lack effective hierarchical interaction, leading to poor integration of semantic and detailed information and high computational costs.

Purpose of the Study:

To develop an efficient semantic segmentation model for accurate pine wilt disease identification from UAV remote sensing imagery.
To address limitations of existing methods by effectively fusing CNN and Transformer representations.
To create a computationally efficient model suitable for edge deployment in real-time forestry monitoring.

Main Methods:

Proposed PGCNet model, a semantic segmentation network that fuses Convolutional Neural Network (CNN) and Transformer features.
Utilized CSWin Transformer as the backbone for comprehensive global contextual information.
Introduced a Progressive Guidance Fusion Module (PGFM) with spatial-channel attention for cross-layer feature fusion.
Incorporated a lightweight Context-Aware Residual Atrous Spatial Pyramid Pooling (CAR-ASPP) module for multi-scale feature enhancement and reduced complexity.

Main Results:

PGCNet demonstrated superior performance over mainstream semantic segmentation models across various metrics.
The model showed strong accuracy in complex backgrounds and for identifying small-scale disease targets.
Achieved high accuracy with excellent computational efficiency, outperforming existing methods.

Conclusions:

PGCNet offers a practical and efficient solution for real-time pine wilt disease detection using UAV imagery.
The model's computational efficiency makes it suitable for edge computing environments in forestry.
The approach shows potential for broader application in agricultural remote sensing for disease identification.