Detection of the Pine Wilt Disease Tree Candidates for Drone Remote Sensing Using Artificial Intelligence Techniques

Syifa, Mutiara; Park, Sungjae; Lee, Chang-Wook

doi:10.1016/j.eng.2020.07.001

Cited by 89 publications

(46 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The justification for this is that if the response of PWN outbreak is related to those factors, a more accurate PWN spread can be estimated when the modeling function includes these factors and consider their effects. However, those contributing factors are largely inconsistent across different studies [18][19][20], and therefore a further investigation is still needed. In addition, the performance of modeling techniques can also vary dramatically depending on the application set-up (e.g., the availability of data, data size, data type), which will introduce biases into the conclusion if the appropriate techniques were not used.…”

Section: Introductionmentioning

confidence: 99%

Characterizing Spatial Patterns of Pine Wood Nematode Outbreaks in Subtropical Zone in China

et al. 2021

View full text Add to dashboard Cite

Pine wood nematode (PWN), Bursaphelenchus xyophilus, originating from North America, has caused great ecological and economic hazards to pine trees worldwide, especially affecting the coniferous forests and mixed forests of masson pine in subtropical regions of China. In order to prevent PWN disease expansion, the risk level and susceptivity of PWN outbreaks need to be predicted in advance. For this purpose, we established a prediction model to estimate the susceptibility and risk level of PWN with vegetation condition variables, anthropogenic activity variables, and topographic feature variables across a large-scale district. The study was conducted in Dangyang City, Hubei Province in China, which was located in a subtropical zone. Based on the location of PWN points derived from airborne imagery and ground survey in 2018, the predictor variables were conducted with remote sensing and geographical information system (GIS) data, which contained vegetation indices including normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR), and normalized red edge index (NDRE) from Sentinel-2 imagery in the previous year (2107), the distance to different level roads which indicated anthropogenic activity, topographic variables in including elevation, slope, and aspect. We compared the fitting effects of different machine learning algorithms such as random forest (RF), K-neighborhood (KNN), support vector machines (SVM), and artificial neural networks (ANN) and predicted the probability of the presence of PWN disease in the region. In addition, we classified PWN points to different risk levels based on the density distribution of PWN sites and built a PWN risk level model to predict the risk levels of PWN outbreaks in the region. The results showed that: (1) the best model for the predictive probability of PWN presence is the RF classification algorithm. For the presence prediction of the dead trees caused by PWN, the detection rate (DR) was 96.42%, the false alarm rate (FAR) was 27.65%, the false detection rate (FDR) was 4.16%, and the area under the receiver operating characteristic curve (AUC) was equal to 0.96; (2) anthropogenic activity variables had the greatest effect on PWN occurrence, while the effects of slope and aspect were relatively weak, and the maximum, minimum, and median values of remote sensing indices were more correlated with PWN occurrence; (3) modeling analysis of different risk levels of PWN outbreak indicated that high-risk level areas were the easiest to monitor and identify, while lower incidence areas were identified with relatively low accuracy. The overall accuracy of the risk level of the PWN outbreak was identified with an AUC value of 0.94. From the research findings, remote sensing data combined with GIS data can accurately predict the probability distribution of the occurrence of PWN disease. The accuracy of identification of high-risk areas is higher than other risk levels, and the results of the study may improve control of PWN disease spread.

show abstract

Section: Introductionmentioning

confidence: 99%

Characterizing Spatial Patterns of Pine Wood Nematode Outbreaks in Subtropical Zone in China

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Since the deep learning can reveal the nonlinear features hidden in the data through multi-layer processing mechanism, and can obtain "feature learning" from a large number of training data sets, the deep learning based detection models have proven to be an effective detection approach. For example in [13]- [16], a UAV and deep learning based detection model was proposed to identify the dead pine trees infected with PWD, where the airborne GNSS (Global Navigation Satellite System) was used to locate the dead pine trees. In [17], Zhang et al use a fixed-wing UAV to collect images of the study area and propose a deep learning network, the U-Net model, to segment the images of wilted pine trees.…”

Section: Introductionmentioning

confidence: 99%

A Remote Sensing and Airborne Edge-Computing Based Detection System for Pine Wilt Disease

Liu

Shen³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

The pine wilt disease (PWD) is one of the most dangerous and destructive diseases to coniferous forests. The rapid spread trend and strong destruction directly threaten the security of forests. The complex spread pattern and the hard labor process of diagnosis call for an effective way to detect the infected areas. In this paper, an airborne edge-computing and lightweight deep learning based system are designed for PWD detection by using imagery sensors. Unmanned aerial vehicle (UAV) is firstly utilized to realize a large-scale coverage of forests, which can substantially reduce the hard labor. Except for infected trees, a large number of irrelevant images are also acquired by the UAV, which will overload the burden of process and transmission. Then a lightweight improved YOLOv4-Tiny based method (named as YOLOv4-Tiny-3Layers) is proposed to filter these uninterested images by leveraging the computation capability of edge computing, which can realize a fast coarse-grained detection with a low missing rate. Finally, all the remaining images are transmitted to the ground workstation for the final fine-grained detection. Experimental results show that the proposed system can implement a fast detection with superior performance as compared to other methods, which helps to detect the infected pine trees in a quick manner.INDEX TERMS pine wilt disease; remote sensing; airborne edge computing; lightweight deep learning; two-stage detection

show abstract

“…Then, they adopted an algorithm based on random forests to identify infected pines. Syifa et al [20] used an UAV to collect RGB (red-green-blue) color images and an artificial neural network and a support vector machine to detect candidate PWD-infected pines, achieving accuracies of 79.33% and 86.59% for evaluation in Wonchang.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Deep Learning Segmentation Models for Detection of Pine Wilt Disease in Unmanned Aerial Vehicle Images

et al. 2021

View full text Add to dashboard Cite

Pine wilt disease (PWD) is a serious threat to pine forests. Combining unmanned aerial vehicle (UAV) images and deep learning (DL) techniques to identify infected pines is the most efficient method to determine the potential spread of PWD over a large area. In particular, image segmentation using DL obtains the detailed shape and size of infected pines to assess the disease’s degree of damage. However, the performance of such segmentation models has not been thoroughly studied. We used a fixed-wing UAV to collect images from a pine forest in Laoshan, Qingdao, China, and conducted a ground survey to collect samples of infected pines and construct prior knowledge to interpret the images. Then, training and test sets were annotated on selected images, and we obtained 2352 samples of infected pines annotated over different backgrounds. Finally, high-performance DL models (e.g., fully convolutional networks for semantic segmentation, DeepLabv3+, and PSPNet) were trained and evaluated. The results demonstrated that focal loss provided a higher accuracy and a finer boundary than Dice loss, with the average intersection over union (IoU) for all models increasing from 0.656 to 0.701. From the evaluated models, DeepLLabv3+ achieved the highest IoU and an F1 score of 0.720 and 0.832, respectively. Also, an atrous spatial pyramid pooling module encoded multiscale context information, and the encoder–decoder architecture recovered location/spatial information, being the best architecture for segmenting trees infected by the PWD. Furthermore, segmentation accuracy did not improve as the depth of the backbone network increased, and neither ResNet34 nor ResNet50 was the appropriate backbone for most segmentation models.

show abstract

Detection of the Pine Wilt Disease Tree Candidates for Drone Remote Sensing Using Artificial Intelligence Techniques

Cited by 89 publications

References 26 publications

Characterizing Spatial Patterns of Pine Wood Nematode Outbreaks in Subtropical Zone in China

Characterizing Spatial Patterns of Pine Wood Nematode Outbreaks in Subtropical Zone in China

A Remote Sensing and Airborne Edge-Computing Based Detection System for Pine Wilt Disease

Evaluation of Deep Learning Segmentation Models for Detection of Pine Wilt Disease in Unmanned Aerial Vehicle Images

Contact Info

Product

Resources

About