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Distribution map of disease trees coordinates imported into ArcGIS (red points are coordinates of disease trees).
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Pine wilt disease is extremely ruinous to forests. It is an important to hold back the transmission of the disease in order to detect diseased trees on UAV imagery, by using a detection algorithm. However, most of the existing detection algorithms for diseased trees ignore the interference of complex backgrounds to the diseased tree feature extract...
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... diseased trees were identified, and the identification results are shown in Table 7. The coordinates of the identified diseased trees were imported into ArcGIS, and the visualization results were shown in Figure 7. Figure 7. Distribution map of disease trees coordinates imported into ArcGIS (red points are coordinates of disease trees). ...
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Citations
... Deng et al. [11] utilized UAV images and deep learning, while Han Z et al. [12] proposed the MSSCN model that incorporated spatial attention to detect standing dead trees in different forest environments. Ren et al. [13] introduced a global multi-scale channel adaptation network to improve feature extraction, and Hu et al. [14] proposed a DDYOLOv5 network with Efficient Channel Attention and hybrid dilated convolution modules using UAV images for accurate pine forest disease detection and severity classification. These works have highlighted the growing significance of applying deep learning techniques to address PWD-related challenges. ...
Pine Wilt Disease poses a significant global threat to forests, necessitating swift detection methods. Conventional approaches are resource-intensive but utilizing deep learning on ortho-mapped images obtained from Unmanned Aerial Vehicles offers cost-effective and scalable solutions. This study presents a novel method for Pine Wilt Disease detection and classification using YOLOv8 for segmenting diseased areas, followed by cropping the diseased regions from the original image and applying Deep Metric Learning for classification. We trained a ResNet50 model using semi-hard triplet loss to obtain embeddings, and subsequently trained a Random Forest classifier tasked with identifying tree species and distinguishing false positives. Segmentation was favored over object detection due to its ability to provide pixel-level information, enabling the flexible extension of subsequent bounding boxes. Deep Metric Learning-based classification after segmentation was chosen for its effectiveness in handling visually similar images. The results indicate a mean Intersection over Union of 83.12% for segmentation, with classification accuracies of 98.7% and 90.7% on the validation and test sets, respectively.
... Abdollahnejad et al. [31] innovatively used UAV images as reference data and combines high-resolution satellite platforms with time series data to evaluate and predict forest health status. Ren et al. [32] proposed a Global Multi-Scale Channel Adaptation Network based on circle sampling to better match the circular shape of the diseased trees. Zhang et al. [33] improved YOLOv5 by four attention mechanism modules to detect smaller infected wood in images that covers a large area. ...
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.
... It performs well in large-scale and complex monitoring tasks. A global multi-scale adaptive network is proposed to detect diseased trees in unmanned aerial vehicle (UAV) images, alleviating the interference caused by complex backgrounds in extracting disease tree features (Ren et al. 2022). This approach alleviates the negative impact of complex background regions on the model in the images, thereby improving recognition accuracy. ...
Pine wood nematode (PWN) disease is a severe contagious disease in forests which endangers the health of forests, as it can rapidly kill trees. The detection of regions impacted by this disease plays a crucial role in forest management, remediation efforts and ensuring the overall ecological security of forests. With the advancements in remote sensing technology, remote sensing images offer distinct advantages such as extensive coverage, timely data acquisition and high spatial resolution, making them highly suitable for accurately and efficiently identifying PWN disease areas. This article introduces a deep learning approach for the identification of PWN disease areas using multi-temporal and multi-source remote sensing images. Specifically, a specialized model called the bitemporal image transformer (BIT) model is developed and trained using multi-temporal Beijing-2 and Beijing-3 images acquired at different stages of PWN disease. BIT is a twin network for semantic segmentation with transformer structure and its backbone utilizes the Resnet structure. The input bitemporal image can be represented by several semantic tokens and encoded using a transformer encoder. The contextual information learned from the tokens is subsequently applied back to the pixel space. Finally, transformer decoder is utilized to refine the original features. To evaluate the model’s generalization ability, the best-trained model for different regions affected by pine nematode disease is identified using a transfer learning mechanism. The experimental results demonstrate a significant enhancement in classification accuracy using the proposed method, with a noteworthy 15.23% improvement in F1-score, 9.34% in accuracy, 11.7% in precision, 12.88% in recall, compared to traditional methods. Moreover, the model exhibits impressive generalization ability across various forest regions. In summary, this research introduces a promising approach that utilizes remote sensing images and deep learning techniques as a powerful tool for precisely identifying and managing areas affected by PWN disease.
... The trees detected with abnormal colors in the forest is an essential way to realize the detection of forest pests and diseases. Meanwhile, Monitoring the discoloring trees infected by pests and diseases is an essential means to control the spread of epidemics (Ren et al., 2022). However, the severity of its impact on pests and disease infection for forest canopy can only be determined by biological physiological sampling in the field until now, which its relies on human participation, and the time cost is high in practical applications (Hall et al., 2016). ...
Pest and disease damage to forests cannot be underestimated, so it is essential to detect diseased trees in time and take measures to stop their spread. The detection of discoloration standing trees is one of the important means to effectively control the spread of pests and diseases. In the visible wavelength range, early infected trees do not show significant color changes, which poses a challenge for early detection and is only suitable for monitoring middle and late discolor trees. The spectral resolution of hyperspectral restricts the improvement of its spatial resolution, and there are phenomena of different spectral of the same and foreign objects in the same spectrum, which affect the detection results. In this paper, the method of hyperspectral and CCD image fusion is used to achieve high-precision detection of discoloration standing trees. This paper proposes an improved algorithm MSGF-GLP, which uses multi-scale detail boosting and MTF filter to refine high-resolution data. By combining guided filtering with hyperspectral images, the spatial detail difference is enhanced, and the injection gain is interpolated into the difference of each band, so as to obtain high-resolution and high-quality hyperspectral images. This research is based on hyperspectral and CCD data obtained from LiCHy, Chinese Academy of Forestry, Maoershan Experimental Forest Farm, Shangzhi City, Heilongjiang Province. The evaluation framework is used to compare with the other five fusion algorithms to verify the good effect of the proposed method, which can effectively preserve the canopy spectrum and improve the spatial details. The fusion results of forestry remote sensing data were analyzed using the vegetation Normalized Difference Water Index and Plant Senescence Reflectance Index. The fused results can be used to distinguish the difference between discoloration trees and healthy trees by the multispectral vegetation index. The research results can provide good technical support for the practical application of forest remote sensing data fusion, and lay the foundation for promoting the scientific, automatic and intelligent forestry control.
... In recent years, the utilization of UAV remote sensing technology has gained significant attention in a variety of fields, such as agriculture [80][81][82][83], forestry [84][85][86], environmental monitoring [87][88][89], and infrastructure inspection [55,67,90]. UAVs provide high-resolution and accurate data that can facilitate informed decision-making and support diverse applications. ...
... This method is based on remote sensing RGB data from a UAV and uses the YOLOv4 [111] tiny neural network model to detect the infection of pine with fusarium wilt. Ren et al. [84] proposed a neural network method for detecting pine wilt from UAV remote sensing RGB image data. The spatial resolution of the RGB image sampled by the authors was 5 cm/pixel. ...
... In terms of forestry disease monitoring, taking the monitoring of pine blight as an example, Ren et al. [84] proposed a method based on UAV remote sensing RGB images with an accuracy of 79.8%; Li et al. [110] proposed a method based on UAV remote sensing hyper-spectral data, with an accuracy from 84% to 99.8%; However, Zhang et al. [202] used data obtained from remote sensing satellites, and their accuracy rate for similar diseases was only 67.7%. ...
In recent years, UAV remote sensing has gradually attracted the attention of scientific researchers and industry, due to its broad application prospects. It has been widely used in agriculture, forestry, mining, and other industries. UAVs can be flexibly equipped with various sensors, such as optical, infrared, and LIDAR, and become an essential remote sensing observation platform. Based on UAV remote sensing, researchers can obtain many high-resolution images, with each pixel being a centimeter or millimeter. The purpose of this paper is to investigate the current applications of UAV remote sensing, as well as the aircraft platforms, data types, and elements used in each application category; the data processing methods, etc.; and to study the advantages of the current application of UAV remote sensing technology, the limitations, and promising directions that still lack applications. By reviewing the papers published in this field in recent years, we found that the current application research of UAV remote sensing research can be classified into four categories according to the application field: (1) Precision agriculture, including crop disease observation, crop yield estimation, and crop environmental observation; (2) Forestry remote sensing, including forest disease identification, forest disaster observation, etc.; (3) Remote sensing of power systems; (4) Artificial facilities and the natural environment. We found that in the papers published in recent years, image data (RGB, multi-spectral, hyper-spectral) processing mainly used neural network methods; in crop disease monitoring, multi-spectral data are the most studied type of data; for LIDAR data, current applications still lack an end-to-end neural network processing method; this review examines UAV platforms, sensors, and data processing methods, and according to the development process of certain application fields and current implementation limitations, some predictions are made about possible future development directions.
... Various PWD detection methods have been proposed using drones equipped with visible light equipment according to differences in appearance between diseased and healthy pine trees. Ren et al. [6] used a new Global Multi-Scale Channel Adaptation Network to detect pine wood nematode trees; the average precision (AP) and the recall are 79.8% and 86.6%, respectively. Zhang et al. [7] proposed a U-Net-based discolored wood recognition method with imagery collected by fixed-wing UAVs and achieved an average accuracy of 95.17%. ...
... In disease tree identification, more and more scholars have used deep learning models [6][7][8][9][10][11][12][13][14][15][16], such as the YOLO series. Meanwhile, a single visible light and a single multispectral cannot meet the demand for high accuracy. ...
Pine wilt disease (PWD) has become increasingly serious recently and causes great damage to the world’s pine forest resources. The use of unmanned aerial vehicle (UAV)-based remote sensing helps to identify pine nematode trees in time and has become a feasible and effective approach to precisely monitor PWD infection. However, a rapid and high-accuracy detection approach has not been well established in a complex terrain environment. To this end, a deep learning-based pine nematode tree identification method is proposed by fusing visible and multispectral imagery. A UAV equipped with a multispectral camera and a visible camera was used to obtain imagery, where multispectral imagery includes six bands, i.e., red, green, blue, near-infrared, red edge and red edge 750 nm. Two vegetation indexes, NDVI (Normalized Difference Vegetation Index) and NDRE (Normalized Difference Red Edge Index) are extracted as a typical feature according to the reflectance of infected trees in different spectral bands. The YOLOv5 (You Only Look Once v5)-based detection algorithm is adopted and optimized from different aspects to realize the identification of infected pine trees with high detection speed and accuracy. e.g., GhostNet is adopted to reduce the number of model parameters and improve the detection speed; a module combining a CBAM (Convolutional Block Attention Module) and a CA (Coordinate Attention) mechanism is designed to improve the feature extraction for small-scale pine nematode trees; Transformer module and BiFPN (Bidirectional Feature Pyramid Network) structure are applied to improve the feature fusion capability. The experiments show that the mAP@0.5 of the improved YOLOv5 model is 98.7%, the precision is 98.1%, the recall is 97.3%, the average detection speed of single imagery is 0.067 s, and the model size is 46.69 MB. All these metrics outperform other comparison methods. Therefore, the proposed method can achieve a fast and accurate detection of pine nematode trees, providing effective technical support for the control of a pine nematode epidemic.
