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Research Article | Open Access

Exploring the Close-Range Detection of UAV-Based Images on Pine Wilt Disease by an Improved Deep Learning Method

Xinquan Ye1, Jie Pan1,2( ), Gaosheng Liu1, Fan Shao1
College of Forestry, Nanjing Forestry University, Nanjing 210037, China
Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
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Abstract

Pine wilt disease (PWD) is a significantly destructive forest disease. To control the spread of PWD, an urgent need exists for a real-time and efficient method to detect infected trees. However, existing object detection models have often faced challenges in balancing lightweight design and accuracy, particularly in complex mixed forests. To address this, an improvement was made to the YOLOv5s (You Only Look Once version 5s) algorithm, resulting in a real-time and efficient model named PWD-YOLO. First, a lightweight backbone was constructed, composed of multiple connected RepVGG Blocks, significantly enhancing the model’s inference speed. Second, a C2fCA module was designed to incorporate rich gradient information flow and concentrate on key features, thereby preserving more detailed characteristics of PWD-infected trees. In addition, the GSConv network was utilized instead of conventional convolutions to reduce network complexity. Last, the Bidirectional Feature Pyramid Network strategy was used to enhance the propagation and sharing of multiscale features. The results demonstrate that on a self-built dataset, PWD-YOLO surpasses existing object detection models with respective measurements of model size (2.7 MB), computational complexity (3.5 GFLOPs), parameter volume (1.09 MB), and speed (98.0 frames/s). The Precision, Recall, and F1-score on the test set are 92.5%, 95.3%, and 93.9%, respectively, which confirms the effectiveness of the proposed method. It provides reliable technical support for daily monitoring and clearing of infected trees by forestry management departments.

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Plant Phenomics
Volume 5,
2023
Article number: 0129
DOI: 10.34133/plantphenomics.0129
Cite this article:
Ye X, Pan J, Liu G, et al. Exploring the Close-Range Detection of UAV-Based Images on Pine Wilt Disease by an Improved Deep Learning Method. Plant Phenomics, 2023, 5: 0129. https://doi.org/10.34133/plantphenomics.0129

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Received: 05 September 2023
Accepted: 23 November 2023
Published: 15 December 2023
© 2023 Xinquan Ye et al. Exclusive licensee Nanjing Agricultural University. No claim to original U.S. Government Works.

Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0).
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