Detection of Pine Wilt Disease Using Time Series UAV Imagery and Deep Learning Semantic Segmentation

Lee, Min-Gyu; Cho, Hyun-Baum; Youm, Sungkwan; Kim, S.

doi:10.3390/f14081576

Cited by 13 publications

(5 citation statements)

References 24 publications

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“…14 With the emergence of deep learning, various object detection algorithms based on deep learning techniques have gradually achieved success in fields like forest pest and disease identification. 16 Examples of such algorithms include YOLO, 17 region convolutional neural networks (R-CNN), 18 single shot multibox detector (SSD), 19 mask-RCNN, 20 Deeplab V3+, 21 and so on. Compared with traditional machine learning, deep learning technology can effectively improve the problems of repeated calculations and low efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…However, a challenge remains in the current integration of deep learning and UAV remote sensing for PWD monitoring applications. Existing researches 20,21 in forestry pest and disease monitoring is based on a fully annotated dataset of infested trees, that is each diseased tree in the training area must be marked separately. In practice, only a subset of the entire UAV remote sensing images is selected for single-tree level annotation.…”

Section: Introductionmentioning

confidence: 99%

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Extraction of pine wilt disease based on a two-stage unmanned aerial vehicle deep learning method

Huang,

Gang,

et al. 2024

J. Appl. Rem. Sens.

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Forestry pests pose a significant threat to forest health, making precise extraction of infested trees a vital aspect of forest protection. In recent years, deep learning has achieved substantial success in detecting infestations. However, when applying existing deep learning methods to infested tree detection, challenges arise, such as limited training samples and confusion between forest areas and artificial structures. To address these issues, this work proposes a two-stage hierarchical semi-supervised deep learning approach based on unmanned aerial vehicle visible images to achieve the individual extraction of each pine wilt disease (PWD). The approach can automatically detect the positions and crown extents of each infested tree. The comprehensive framework includes the following key steps: (a) considering the disparities in global image representation between forest areas and artificial structures, a scene classification network named MobileNetV3 is trained to effectively differentiate between forested regions and other artificial structures. (b) Considering the high cost of manually annotating and incomplete labeling of infested tree samples, a semi-supervised infested tree samples mining method is introduced, significantly reducing the workload of sample annotation. Ultimately, this method is integrated into the YOLOv7 object detection network, enabling rapid and reliable detection of infested trees. Experimental results demonstrate that, with a confidence threshold of 0.15 and using the semi-supervised sample mining framework, the number of samples increases from 53,046 to 93,544. Precision evaluation metrics indicate a 5.8% improvement in recall and a 2.6% increase in mean average precision@.5. The final test area prediction achieves an overall accuracy of over 80% and the recall rate of over 90%, indicating the effectiveness of the proposed method in PWD detection.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extraction of pine wilt disease based on a two-stage unmanned aerial vehicle deep learning method

Huang,

Gang,

et al. 2024

J. Appl. Rem. Sens.

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“…Semantic segmentation, a technique based on CNNs, enables simultaneous segmentation and classification of various objects. In forestry, deep learning analysis has been applied to detect forest fires, landslides, pest infestation, tree species classification, and land cover classification [15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

ResNet152-U-Net: Transfer Learning for the Semantic Segmentation of Forest Restoration Site Using Aerial Images

Son,

Ha,

Seong

et al. 2024

Preprint

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Accurate detection of forest restoration sites using deep learning is crucial for effective ecosystem management, optimizing reforestation efforts, and ensuring environmental sustainability. While various deep learning-based systems have been developed for this purpose, the influence of different neural network architectures on model performance during the identification of forest restoration sites remains underexplored. This study aimed to address this gap by exploring the optimal methodology for extracting and classifying candidate sites for forest restoration based on Convolutional Neural Networks (CNNs), specialized for image recognition. Four categories—arable land, road and barren, quarry, and forest—were defined as candidate sites for forest restoration. A dataset comprising 17,043 samples was divided into training (11,929) and validation (5,114) sets at a ratio of 7:3 for model learning. Model accuracy was evaluated using pixel accuracy (PA) and mean intersection over union (mean IoU). The Resnet152-U-Net model demonstrated a pixel accuracy of 95.2% and a mean IoU of 61.3%, indicating excellent performance in extracting candidate sites for forest restoration. This approach offers spatial and temporal advantages over conventional field surveys or aerial image-based assessments, potentially serving as valuable data for selecting future forest restoration sites.

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“…Key among these challenges are slow training speeds and the difficulty in achieving high-quality segmentation results, especially for fine segmentation tasks. Although deep learning methods have demonstrated their effectiveness in pine nematode tree monitoring in recent years [19][20][21][22][23], traditional approaches can still lead to the loss of crucial details during the feature extraction process. This loss of details can significantly impact the accuracy of segmentation results, especially in the case of pine nematode infestation, where preserving key information is vital for subsequent segmentation tasks.…”

Section: Introductionmentioning

confidence: 99%

Aerial Image Segmentation of Nematode-Affected Pine Trees with U-Net Convolutional Neural Network

Shen,

Xu,

Gao

et al. 2024

Applied Sciences

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Pine wood nematode disease, commonly referred to as pine wilt, poses a grave threat to forest health, leading to profound ecological and economic impacts. Originating from the pine wood nematode, this disease not only causes the demise of pine trees but also casts a long shadow over the entire forest ecosystem. The accurate identification of infected trees stands as a pivotal initial step in developing effective prevention and control measures for pine wilt. Nevertheless, existing identification methods face challenges in precisely determining the disease status of individual pine trees, impeding early detection and efficient intervention. In this study, we leverage the capabilities of unmanned aerial vehicle (UAV) remote sensing technology and integrate the VGG classical small convolutional kernel network with U-Net to detect diseased pine trees. This cutting-edge approach captures the spatial and characteristic intricacies of infected trees, converting them into high-dimensional features through multiple convolutions within the VGG network. This method significantly reduces the parameter count while enhancing the sensing range. The results obtained from our validation set are remarkably promising, achieving a Mean Intersection over Union (MIoU) of 81.62%, a Mean Pixel Accuracy (MPA) of 85.13%, an Accuracy of 99.13%, and an F1 Score of 88.50%. These figures surpass those obtained using other methods such as ResNet50 and DeepLab v3+. The methodology presented in this research facilitates rapid and accurate monitoring of pine trees infected with nematodes, offering invaluable technical assistance in the prevention and management of pine wilt disease.

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Detection of Pine Wilt Disease Using Time Series UAV Imagery and Deep Learning Semantic Segmentation

Cited by 13 publications

References 24 publications

Extraction of pine wilt disease based on a two-stage unmanned aerial vehicle deep learning method

Extraction of pine wilt disease based on a two-stage unmanned aerial vehicle deep learning method

ResNet152-U-Net: Transfer Learning for the Semantic Segmentation of Forest Restoration Site Using Aerial Images

Aerial Image Segmentation of Nematode-Affected Pine Trees with U-Net Convolutional Neural Network

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