Semi-Supervised Learning Method for Pine Wilt Disease Detection in UAV Images

WANG Chang; XIONG Hanjiang; TU Jianguang; ZHENG Xianwei

doi:10.13203/j.whugis20220634

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Semi-Supervised Learning Method for Pine Wilt Disease Detection in UAV Images

Photogrammetry and Remote Sensing | 更新时间：2026-11-05

- Semi-Supervised Learning Method for Pine Wilt Disease Detection in UAV Images
- Geomatics and Information Science of Wuhan University Vol. 50, Issue 12, Pages: 2560-2568(2025)
- 作者机构：
  
  1.武汉大学测绘遥感信息工程全国重点实验室，湖北武汉，430079
  2.武汉大学遥感信息工程学院，湖北武汉，430079
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20220634
  CLC： P237
- Received：29 September 2023，
  
  Published：05 December 2025
- 稿件说明：
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王畅,熊汉江,涂建光，等.无人机影像的松材线虫病半监督学习检测方法[J].武汉大学学报（信息科学版）,2025,50(12):2560-2568.

WANG Chang,XIONG Hanjiang,TU Jianguang,et al.Semi-Supervised Learning Method for Pine Wilt Disease Detection in UAV Images[J].Geomatics and Information Science of Wuhan University,2025,50(12):2560-2568.
王畅,熊汉江,涂建光，等.无人机影像的松材线虫病半监督学习检测方法[J].武汉大学学报（信息科学版）,2025,50(12):2560-2568. DOI：10.13203/j.whugis20220634

WANG Chang,XIONG Hanjiang,TU Jianguang,et al.Semi-Supervised Learning Method for Pine Wilt Disease Detection in UAV Images[J].Geomatics and Information Science of Wuhan University,2025,50(12):2560-2568. DOI：10.13203/j.whugis20220634

摘要

松材线虫病是对森林具有破坏性威胁的全球性疾病，每年在中国造成了极大的生态和经济损失，快速准确地监测和绘制松树的感染状况对于控制此类病虫害传播至关重要。为解决在少量有病害标签数据下用大量无标签数据提高模型精确率的问题，提出了一种基于深度学习的半监督目标检测方法，联合已有的标记数据和非标记数据训练YOLOv5目标检测模型，利用大范围无人机快拼图像快速自动地识别定位出感染松材线虫病的单株变色木。实验结果表明，半监督深度学习和无人机遥感结合能有效识别出疫木，精确率可达到85%以上，漏检率为9%，经过实际业务数据盲盒验证，所提算法指标满足大面积松材线虫病疫情快速动态监测要求。

Abstract

Objectives

Pine wilt disease (PWD) is a global disease with devastating threat to forests

and causes great ecological and economic losses in China every year. It is important to monitor and map the infection status of pine trees accurately for industry sectors to control the spread of such diseases timely. Traditional PWD detection methods such as manual inspection and visual interpretation of remote sensing images are inefficient. Recently

deep learning algorithms are applied in PWD detection task

bringing significant performance gains. However

the current methods usually adopt supervised learning strategy

which requires lots of professional labeling that is time-consuming and labor-intensive.

Methods

We propose a semi-supervised PWD detection method in a teacher-student mechanism. First

16 310 images are collected in Wudu River Region in Yichang City

Hubei Province

with 697 labeled images for model training and 15 613 unlabeled images for testing. Then

YOLOv5 model is adopted as a student model to learn features of input images

and the learnt features are updated by exponential moving average algorithm to build a teacher model for generating pseudo-labels of unlabeled test images. The test images with pseudo-labels are mixed with train dataset and fed into the student model to optimize features and generate a stronger teacher model. With the corporation of student and teacher model

the network is more capable to learn robust features for detecting PWD in UAV images.

Results

The extensive experiments demonstrate that the proposed semi-supervised method can effectively detect the diseased wood and achieve a promising performance with precision of 85.05% and recall of 91.36% on the test dataset

the ground truth of which is provided by third-party professionals.

Conclusions

The proposed semi-supervised object detection method uses a small portion of labeled data to robustly learn features in large scale UAV images for high-quality PWD detection. The performance on test dataset shows that the proposed method could be well applied to the application of rapid dynamic monitoring of PWD epidemics in forestry field

which could benefit for the sustainable development of forest ecosystem.

关键词

Keywords

references

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