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

2021 ◽  
Vol 13 (18) ◽  
pp. 3594
Author(s):  
Lang Xia ◽  
Ruirui Zhang ◽  
Liping Chen ◽  
Longlong Li ◽  
Tongchuan Yi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
High Performance ◽  
Spatial Information ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Large Area ◽  
Pine Wilt ◽  
Aerial Vehicle ◽  
Segmentation Models

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.

scholarly journals Detection and Location of Dead Trees with Pine Wilt Disease Based on Deep Learning and UAV Remote Sensing

2020 ◽  
Vol 2 (2) ◽  
pp. 294-307 ◽  
Author(s):  
Xiaoling Deng ◽  
Zejing Tong ◽  
Yubin Lan ◽  
Zixiao Huang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Pine Wood Nematode ◽  
Dead Tree ◽  
Pine Wood ◽  
Tree Detection ◽  
Pine Wilt

Pine wilt disease causes huge economic losses to pine wood forestry because of its destructiveness and rapid spread. This paper proposes a detection and location method of pine wood nematode disease at a large scale adopting UAV (Unmanned Aerial Vehicle) remote sensing and artificial intelligence technology. The UAV remote sensing images were enhanced by computer vision tools. A Faster-RCNN (Faster Region Convolutional Neural Networks) deep learning framework based on a RPN (Region Proposal Network) network and the ResNet residual neural network were used to train the pine wilt diseased dead tree detection model. The loss function and the anchors in the RPN of the convolutional neural network were optimized. Finally, the location of pine wood nematode dead tree was conducted, which generated the geographic information on the detection results. The results show that ResNet101 performed better than VGG16 (Visual Geometry Group 16) convolutional neural network. The detection accuracy was improved and reached to about 90% after a series of optimizations to the network, meaning that the optimization methods proposed in this paper are feasible to pine wood nematode dead tree detection.

scholarly journals Early detection of pine wilt disease using deep learning algorithms and UAV-based multispectral imagery

2021 ◽  
Vol 497 ◽  
pp. 119493
Author(s):  
Run Yu ◽  
Youqing Luo ◽  
Quan Zhou ◽  
Xudong Zhang ◽  
Dewei Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Detection ◽  
Learning Algorithms ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Multispectral Imagery ◽  
Pine Wilt

Application of conventional UAV-based high-throughput object detection to the early diagnosis of pine wilt disease by deep learning

2021 ◽  
Vol 486 ◽  
pp. 118986
Author(s):  
Bizhi Wu ◽  
Anjie Liang ◽  
Huafeng Zhang ◽  
Tengfei Zhu ◽  
Zhiying Zou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Diagnosis ◽  
Object Detection ◽  
High Throughput ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Pine Wilt

scholarly journals Use of Unmanned Aerial Vehicle Imagery and Deep Learning UNet to Extract Rice Lodging

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3859 ◽  
Author(s):  
Zhao ◽  
Yuan ◽  
Song ◽  
Ding ◽  
Lin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Optical Sensors ◽  
Network Architecture ◽  
High Efficiency ◽  
Low Cost ◽  
Digital Camera ◽  
Large Area ◽  
Image Test ◽  
Aerial Vehicle

Rice lodging severely affects harvest yield. Traditional evaluation methods and manual on-site measurement are found to be time-consuming, labor-intensive, and cost-intensive. In this study, a new method for rice lodging assessment based on a deep learning UNet (U-shaped Network) architecture was proposed. The UAV (unmanned aerial vehicle) equipped with a high-resolution digital camera and a three-band multispectral camera synchronously was used to collect lodged and non-lodged rice images at an altitude of 100 m. After splicing and cropping the original images, the datasets with the lodged and non-lodged rice image samples were established by augmenting for building a UNet model. The research results showed that the dice coefficients in RGB (Red, Green and Blue) image and multispectral image test set were 0.9442 and 0.9284, respectively. The rice lodging recognition effect using the RGB images without feature extraction is better than that of multispectral images. The findings of this study are useful for rice lodging investigations by different optical sensors, which can provide an important method for large-area, high-efficiency, and low-cost rice lodging monitoring research.

scholarly journals Effects of cavitation on the development of pine wilt disease caused by Bursaphelenchus xylophilus.

1988 ◽  
Vol 54 (5) ◽  
pp. 606-615 ◽  
Author(s):  
Keiko KURODA ◽  
Toshihiro YAMADA ◽  
Kazuhiko MINEO ◽  
Hirotada TAMURA
Keyword(s):  
Pine Wilt Disease ◽  
Bursaphelenchus Xylophilus ◽  
Wilt Disease ◽  
Pine Wilt

scholarly journals Early detection of pine wilt disease in Pinus tabuliformis in North China using a field portable spectrometer and UAV-based hyperspectral imagery

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Run Yu ◽  
Lili Ren ◽  
Youqing Luo
Keyword(s):  
Early Stage ◽  
Vegetation Indices ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Pinus Tabulaeformis ◽  
Pine Tree ◽  
Preventative Measures ◽  
Pine Wilt ◽  
Pine Trees ◽  
Airborne Vehicle

Abstract Background Pine wilt disease (PWD) is a major ecological concern in China that has caused severe damage to millions of Chinese pines (Pinus tabulaeformis). To control the spread of PWD, it is necessary to develop an effective approach to detect its presence in the early stage of infection. One potential solution is the use of Unmanned Airborne Vehicle (UAV) based hyperspectral images (HIs). UAV-based HIs have high spatial and spectral resolution and can gather data rapidly, potentially enabling the effective monitoring of large forests. Despite this, few studies examine the feasibility of HI data use in assessing the stage and severity of PWD infection in Chinese pine. Method To fill this gap, we used a Random Forest (RF) algorithm to estimate the stage of PWD infection of trees sampled using UAV-based HI data and ground-based data (data directly collected from trees in the field). We compared relative accuracy of each of these data collection methods. We built our RF model using vegetation indices (VIs), red edge parameters (REPs), moisture indices (MIs), and their combination. Results We report several key results. For ground data, the model that combined all parameters (OA: 80.17%, Kappa: 0.73) performed better than VIs (OA: 75.21%, Kappa: 0.66), REPs (OA: 79.34%, Kappa: 0.67), and MIs (OA: 74.38%, Kappa: 0.65) in predicting the PWD stage of individual pine tree infection. REPs had the highest accuracy (OA: 80.33%, Kappa: 0.58) in distinguishing trees at the early stage of PWD from healthy trees. UAV-based HI data yielded similar results: the model combined VIs, REPs and MIs (OA: 74.38%, Kappa: 0.66) exhibited the highest accuracy in estimating the PWD stage of sampled trees, and REPs performed best in distinguishing healthy trees from trees at early stage of PWD (OA: 71.67%, Kappa: 0.40). Conclusion Overall, our results confirm the validity of using HI data to identify pine trees infected with PWD in its early stage, although its accuracy must be improved before widespread use is practical. We also show UAV-based data PWD classifications are less accurate but comparable to those of ground-based data. We believe that these results can be used to improve preventative measures in the control of PWD.

scholarly journals Identifying the Branch of Kiwifruit Based on Unmanned Aerial Vehicle (UAV) Images Using Deep Learning Method

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4442
Author(s):  
Zijie Niu ◽  
Juntao Deng ◽  
Xu Zhang ◽  
Jun Zhang ◽  
Shijia Pan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Semantic Segmentation ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Distribution Maps ◽  
Time Operation ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Segmentation Image

It is important to obtain accurate information about kiwifruit vines to monitoring their physiological states and undertake precise orchard operations. However, because vines are small and cling to trellises, and have branches laying on the ground, numerous challenges exist in the acquisition of accurate data for kiwifruit vines. In this paper, a kiwifruit canopy distribution prediction model is proposed on the basis of low-altitude unmanned aerial vehicle (UAV) images and deep learning techniques. First, the location of the kiwifruit plants and vine distribution are extracted from high-precision images collected by UAV. The canopy gradient distribution maps with different noise reduction and distribution effects are generated by modifying the threshold and sampling size using the resampling normalization method. The results showed that the accuracies of the vine segmentation using PSPnet, support vector machine, and random forest classification were 71.2%, 85.8%, and 75.26%, respectively. However, the segmentation image obtained using depth semantic segmentation had a higher signal-to-noise ratio and was closer to the real situation. The average intersection over union of the deep semantic segmentation was more than or equal to 80% in distribution maps, whereas, in traditional machine learning, the average intersection was between 20% and 60%. This indicates the proposed model can quickly extract the vine distribution and plant position, and is thus able to perform dynamic monitoring of orchards to provide real-time operation guidance.

Dynamical aspects of pine wilt disease and control measures

2021 ◽  
Vol 145 ◽  
pp. 110764
Author(s):  
Takasar Hussain ◽  
Adnan Aslam ◽  
Muhammad Ozair ◽  
Fatima Tasneem ◽  
J.F. Gómez-Aguilar
Keyword(s):  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Control Measures ◽  
Pine Wilt ◽  
And Control

scholarly journals Spatiotemporal Pattern of Pine Wilt Disease in the Yangtze River Basin

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 731
Author(s):  
Zhuoqing Hao ◽  
Jixia Huang ◽  
Yantao Zhou ◽  
Guofei Fang
Keyword(s):  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Spatiotemporal Pattern ◽  
Pinewood Nematode ◽  
The Yangtze River ◽  
Pine Wilt ◽  
The Yangtze River Basin

The Yangtze River Basin is among the river basins with the strongest strategic support and developmental power in China. As an invasive species, the pinewood nematode (PWN) Bursaphelenchus xylophilus has introduced a serious obstacle to the high-quality development of the economic and ecological synchronization of the Yangtze River Basin. This study analyses the occurrence and spread of pine wilt disease (PWD) with the aim of effectively managing and controlling the spread of PWD in the Yangtze River Basin. In this study, statistical data of PWD-affected areas in the Yangtze River Basin are used to analyse the occurrence and spread of PWD in the study area using spatiotemporal visualization analysis and spatiotemporal scanning statistics technology. From 2000 to 2018, PWD in the study area showed an “increasing-decreasing-increasing” trend, and PWD increased explosively in 2018. The spatial spread of PWD showed a “jumping propagation-multi-point outbreak-point to surface spread” pattern, moving west along the river. Important clusters were concentrated in the Jiangsu-Zhejiang area from 2000 to 2015, forming a cluster including Jiangsu and Zhejiang. Then, from 2015–2018, important clusters were concentrated in Chongqing. According to the spatiotemporal scanning results, PWD showed high aggregation in the four regions of Zhejiang, Chongqing, Hubei, and Jiangxi from 2000 to 2018. In the future, management systems for the prevention and treatment of PWD, including ecological restoration programs, will require more attention.

Can insecticide applications used to kill vector insects prevent pine wilt disease?

2021 ◽  
Author(s):  
Jong‐Kook Jung ◽  
Ung Gyu Lee ◽  
Deokjea Cha ◽  
Dong Soo Kim ◽  
Chansik Jung
Keyword(s):  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Pine Wilt
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