Automatic pine wilt disease detection based on improved YOLOv8 UAV multispectral imagery

IF 5.8 2区环境科学与生态学 Q1 ECOLOGY

Ecological Informatics Pub Date : 2024-10-23 DOI:10.1016/j.ecoinf.2024.102846

Shaoxiong Xu , Wenjiang Huang , Dacheng Wang , Biyao Zhang , Hong Sun , Jiayu Yan , Jianli Ding , Jinjie Wang , Qiuli Yang , Tiecheng Huang , Xu Ma , Longlong Zhao , Zhuoqun Du

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引用次数: 0

Abstract

The pine wilt disease (PWD) can cause destructive death to pine trees in a short period. Utilizing unmanned aerial vehicle (UAV) remote sensing technology to promptly identify PWD-infected trees has become an effective and feasible method for precise PWD monitoring. In this study, UAV multispectral imagery was used to analyze the sensitive spectral bands and different vegetation indices for PWD discriminability. A dataset of optimal spectral combinations from visible light and multispectral images was constructed, along with an improved YOLOv8 deep learning model for rapid and accurate identification of PWD-infected trees. The improved YOLOv8 model used omni-dimensional dynamic convolution (ODConv) to enhance the performance of convolutional networks, designed a dynamic head (DyHead) module to capture PWD features more accurately, and applied MPDioU to improve the regression accuracy and model runtime efficiency. Experimental results showed that the [email protected] of the improved YOLOv8 model increased to 89.1 %, with a user accuracy of 90 % and a recall rate of 93.1 %. This achieved rapid and accurate detection of PWD-infected trees, providing effective technical support for automatic identification of PWD epidemic areas and control of PWD outbreaks based on UAV multispectral imagery.

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基于改进型 YOLOv8 无人机多光谱图像的松树枯萎病自动检测

松树枯萎病（PWD）可在短时间内造成松树毁灭性死亡。利用无人机（UAV）遥感技术来及时识别受松树枯萎病感染的树木，已成为精确监测松树枯萎病的一种有效可行的方法。本研究利用无人机多光谱图像分析了敏感光谱波段和不同的植被指数，以鉴别树木是否感染病虫害。研究人员从可见光和多光谱图像中构建了一个最佳光谱组合数据集，并建立了一个改进的 YOLOv8 深度学习模型，用于快速、准确地识别受 PWD 感染的树木。改进后的YOLOv8模型使用了全维动态卷积（ODConv）来提高卷积网络的性能，设计了动态头（DyHead）模块来更准确地捕捉PWD特征，并应用MPDioU来提高回归精度和模型运行效率。实验结果表明，改进后的YOLOv8模型的[email protected]提高到了89.1%，用户准确率为90%，召回率为93.1%。这就实现了对感染了PWD的树木的快速准确检测，为基于无人机多光谱影像自动识别PWD疫区和控制PWD疫情提供了有效的技术支持。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ecological Informatics 环境科学-生态学

CiteScore

8.30

自引率

11.80%

发文量

346

审稿时长

46 days

期刊介绍： The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.