Detecting Pine Trees Damaged by Wilt Disease Using Deep Learning Techniques Applied to Multi-Spectral Images

Zhang, Ruirui; Lee, Joonwhoan; Jie, You

doi:10.1109/access.2022.3155531

Cited by 12 publications

(4 citation statements)

References 31 publications

(30 reference statements)

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“…Compared to Zhang et al(Zhang et al, 2022a) who utilized the improved DenseNet to detect pine wilt diseased trees, YOLOv5L-s shows slightly lower accuracy, but it's model size and number of model parameters are much smaller. The reduction of model size and number of model parameters has great significance to make the model more adaptable to low configuration running environments and can be more easily deployed on mobile devices in the future.…”

mentioning

confidence: 86%

Pine wilt disease detection algorithm based on improved YOLOv5

Du,

Wu,

Wen

et al. 2024

Front. Plant Sci.

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Pine wilt disease (PWD) poses a significant threat to forests due to its high infectivity and lethality. The absence of an effective treatment underscores the importance of timely detection and isolation of infected trees for effective prevention and control. While deep learning techniques combined unmanned aerial vehicle (UAV) remote sensing images offer promise for accurate identification of diseased pine trees in their natural environments, they often demand extensive prior professional knowledge and struggle with efficiency. This paper proposes a detection model YOLOv5L-s-SimAM-ASFF, which achieves remarkable precision, maintains a lightweight structure, and facilitates real-time detection of diseased pine trees in UAV RGB images under natural conditions. This is achieved through the integration of the ShuffleNetV2 network, a simple parameter-free attention module known as SimAM, and adaptively spatial feature fusion (ASFF). The model boasts a mean average precision (mAP) of 95.64% and a recall rate of 91.28% in detecting pine wilt diseased trees, while operating at an impressive 95.70 frames per second (FPS). Furthermore, it significantly reduces model size and parameter count compared to the original YOLOv5-Lite. These findings indicate that the proposed model YOLOv5L-s-SimAM-ASFF is most suitable for real-time, high-accuracy, and lightweight detection of PWD-infected trees. This capability is crucial for precise localization and quantification of infected trees, thereby providing valuable guidance for effective management and eradication efforts.

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confidence: 86%

Pine wilt disease detection algorithm based on improved YOLOv5

Du,

Wu,

Wen

et al. 2024

Front. Plant Sci.

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“…Cai et al [19] proposed an effective data augmentation method based on Sentinel-2 satellite data and UAV images to efficiently detect PWD. Zhang et al [20] corrected 5-band multi-spectral images and visualized them as heat maps to propose a patch-based deep classification. Many researchers also engage in evaluating [21,22] or improving models [23][24][25][26][27][28], such as optimizing neural networks.…”

Section: Introductionmentioning

confidence: 99%

Pine-YOLO: A Method for Detecting Pine Wilt Disease in Unmanned Aerial Vehicle Remote Sensing Images

Yao,

Song,

Chen

et al. 2024

Forests

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Pine wilt disease is a highly contagious forest quarantine ailment that spreads rapidly. In this study, we designed a new Pine-YOLO model for pine wilt disease detection by incorporating Dynamic Snake Convolution (DSConv), the Multidimensional Collaborative Attention Mechanism (MCA), and Wise-IoU v3 (WIoUv3) into a YOLOv8 network. Firstly, we collected UAV images from Beihai Forest and Linhai Park in Weihai City to construct a dataset via a sliding window method. Then, we used this dataset to train and test Pine-YOLO. We found that DSConv adaptively focuses on fragile and curved local features and then enhances the perception of delicate tubular structures in discolored pine branches. MCA strengthens the attention to the specific features of pine trees, helps to enhance the representational capability, and improves the generalization to diseased pine tree recognition in variable natural environments. The bounding box loss function has been optimized to WIoUv3, thereby improving the overall recognition accuracy and robustness of the model. The experimental results reveal that our Pine-YOLO model achieved the following values across various evaluation metrics: MAP@0.5 at 90.69%, mAP@0.5:0.95 at 49.72%, precision at 91.31%, recall at 85.72%, and F1-score at 88.43%. These outcomes underscore the high effectiveness of our model. Therefore, our newly designed Pine-YOLO perfectly addresses the disadvantages of the original YOLO network, which helps to maintain the health and stability of the ecological environment.

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“…Furthermore, a stochastic deep forest algorithm was implemented to monitor pine nematode-infested trees [14]. Another approach for detecting pine diseases involves utilizing orthophoto-corrected 5-band multispectral images from unmanned aerial vehicles (UAVs), paired with a ResNet18 backbone network augmented with a modified DenseNet module for classification [15]. The VDNet network, a fusion of VGG-16 and dilated convolution (DC), was also developed for pine disease detection.…”

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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Detecting Pine Trees Damaged by Wilt Disease Using Deep Learning Techniques Applied to Multi-Spectral Images

Cited by 12 publications

References 31 publications

Pine wilt disease detection algorithm based on improved YOLOv5

Pine wilt disease detection algorithm based on improved YOLOv5

Pine-YOLO: A Method for Detecting Pine Wilt Disease in Unmanned Aerial Vehicle Remote Sensing Images

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

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