Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms

Qin, Jun; Wang, Biao; Wu, Yue; Qi, Lu; Zhu, Haoxiang

doi:10.3390/rs13020162

Cited by 68 publications

(49 citation statements)

References 37 publications

(37 reference statements)

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“…Aerial images [54,135,136], hyperspectral images [137][138][139], and SAR images [97,140,141] were also processed with At-DL methods in 55, 43, and 24 papers, respectively. However, UAV images were used in only three papers [34,94,142]. This is a surprisingly low number; however, due to the very high resolution of the UAV images, the attention mechanism could significantly increase the performance of the DL methods.…”

Section: Rq4 What Are the Used Data Sets/types In Attention-based Deep Learning Methods For Remote Sensing Image Processing?mentioning

confidence: 99%

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

et al. 2021

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Machine learning, particularly deep learning (DL), has become a central and state-of-the-art method for several computer vision applications and remote sensing (RS) image processing. Researchers are continually trying to improve the performance of the DL methods by developing new architectural designs of the networks and/or developing new techniques, such as attention mechanisms. Since the attention mechanism has been proposed, regardless of its type, it has been increasingly used for diverse RS applications to improve the performances of the existing DL methods. However, these methods are scattered over different studies impeding the selection and application of the feasible approaches. This study provides an overview of the developed attention mechanisms and how to integrate them with different deep learning neural network architectures. In addition, it aims to investigate the effect of the attention mechanism on deep learning-based RS image processing. We identified and analyzed the advances in the corresponding attention mechanism-based deep learning (At-DL) methods. A systematic literature review was performed to identify the trends in publications, publishers, improved DL methods, data types used, attention types used, overall accuracies achieved using At-DL methods, and extracted the current research directions, weaknesses, and open problems to provide insights and recommendations for future studies. For this, five main research questions were formulated to extract the required data and information from the literature. Furthermore, we categorized the papers regarding the addressed RS image processing tasks (e.g., image classification, object detection, and change detection) and discussed the results within each group. In total, 270 papers were retrieved, of which 176 papers were selected according to the defined exclusion criteria for further analysis and detailed review. The results reveal that most of the papers reported an increase in overall accuracy when using the attention mechanism within the DL methods for image classification, image segmentation, change detection, and object detection using remote sensing images.

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Section: Rq4 What Are the Used Data Sets/types In Attention-based Deep Learning Methods For Remote Sensing Image Processing?mentioning

confidence: 99%

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

et al. 2021

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“…Compared with 2D CNN, it can directly extract spatial and spectral information from hyperspectral images at the same time, and make us more accurate in identifying PWD-infected pine trees. Additionally, using only 20% of the training samples, the OA and EIP accuracy of the 3D-Res CNN can still achieve 81.06% and 51.97%, which is superior to the state-of-the-art method in the early detection of PWD based on hyperspectral images [11,19,20,31,48]. However, in our study, when we used the proposed model, we performed PCA first instead of directly using the raw data (because the raw data is too enormous), which made our classification process less convenient.…”

Section: Existing Deficiencies and Future Prospectsmentioning

confidence: 96%

“…In another research, Yu et al [20] employed Faster R-CNN and YOLOv4 to identify early infected pine trees by PWD, revealing that early detection of PWD can be optimized by regarding broadleaved trees. Qin et al [31] proposed a new framework, namely spatial-context-attention network (SCANet), to recognize PWD-infected pine trees using UAV images. The study obtained an overall accuracy (OA) of 79% and provided a valuable method to monitor and manage PWD.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Convolutional Neural Network Model for Early Detection of Pine Wilt Disease Using UAV-Based Hyperspectral Images

Luo

et al. 2021

Remote Sensing

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As one of the most devastating disasters to pine forests, pine wilt disease (PWD) has caused tremendous ecological and economic losses in China. An effective way to prevent large-scale PWD outbreaks is to detect and remove the damaged pine trees at the early stage of PWD infection. However, early infected pine trees do not show obvious changes in morphology or color in the visible wavelength range, making early detection of PWD tricky. Unmanned aerial vehicle (UAV)-based hyperspectral imagery (HI) has great potential for early detection of PWD. However, the commonly used methods, such as the two-dimensional convolutional neural network (2D-CNN), fail to simultaneously extract and fully utilize the spatial and spectral information, whereas the three-dimensional convolutional neural network (3D-CNN) is able to collect this information from raw hyperspectral data. In this paper, we applied the residual block to 3D-CNN and constructed a 3D-Res CNN model, the performance of which was then compared with that of 3D-CNN, 2D-CNN, and 2D-Res CNN in identifying PWD-infected pine trees from the hyperspectral images. The 3D-Res CNN model outperformed the other models, achieving an overall accuracy (OA) of 88.11% and an accuracy of 72.86% for detecting early infected pine trees (EIPs). Using only 20% of the training samples, the OA and EIP accuracy of 3D-Res CNN can still achieve 81.06% and 51.97%, which is superior to the state-of-the-art method in the early detection of PWD based on hyperspectral images. Collectively, 3D-Res CNN was more accurate and effective in early detection of PWD. In conclusion, 3D-Res CNN is proposed for early detection of PWD in this paper, making the prediction and control of PWD more accurate and effective. This model can also be applied to detect pine trees damaged by other diseases or insect pests in the forest.

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“…The total number of dataset images reaches 11,237. All images were taken uniformly with a Zeiss professional microscope Axio Imager Z1 with an objective magnification of 40X and a uniform image resolution of 1388*1040 [17]. As shown in Figure 3, it is a schematic diagram of the collected common different kinds of plant nematodes.…”

Section: Datasetmentioning

confidence: 99%

Fast Self-Attention Deep Detection Network Based on Weakly Differentiated Plant Nematodes

Zhuang

Liu

et al. 2021

Preprint

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Background: High-precision, high-speed detection and classification of weakly differentiated targets has always been a difficult problem in the field of image vision. In this paper, the detection of phytopathogenic Bursaphelenchus xylophilus with small size and very weak inter-species differences is taken as an example. Methods: Our work has been carried out in response to the current weakly differentiated target detection problems: a. To replace the complex network labelling and training process based on expert empirical knowledge, we proposed a lightweight Self-Attention network. Experiments proved that the key feature identification areas of plant nematodes found by our Self-Attention network is in good agreement with the customs expert empirical knowledge, and the feature areas found by the method in this paper can better obtain higher detection accuracy than the expert knowledge; b. To optimize the computing power caused by the input of the entire image, we used low-resolution images to quickly obtain the key feature location coordinates, then obtain high-resolution feature areas information based on the coordinates; c. We adopt the adaptive weight with multi-feature joint detection method based on the brightness of the heatmap to further improve the detection accuracy; d. We constructed a more complete high-resolution training dataset involving 24 species of Bursaphelenchus xylophilus and other common hybrid species with a total amount of data exceeded 10,000. Results: The algorithm proposed in this paper replaces the tedious extensive manual labelling in the training process, improves the average training time of the model by more than 50%, reduces the testing time of a single sample by about 27%, optimizes the model storage size by 65%, improves the detection accuracy of the ImageNet pre-trained model by 12.6%, and improves the detection accuracy of the no-ImageNet pre-trained model by more than 48%. Conclusions: Overall, the method in this paper has achieved better results in terms of model complexity, training annotation, as well as testing time and accuracy.

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Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms

Cited by 68 publications

References 37 publications

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

Three-Dimensional Convolutional Neural Network Model for Early Detection of Pine Wilt Disease Using UAV-Based Hyperspectral Images

Fast Self-Attention Deep Detection Network Based on Weakly Differentiated Plant Nematodes

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