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

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.

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A Deep Learning-Based Generalized System for Detecting Pine Wilt Disease Using RGB-Based UAV Images

Remote Sensing ◽

10.3390/rs14010150 ◽

2021 ◽

Vol 14 (1) ◽

pp. 150

Author(s):

Jie You ◽

Ruirui Zhang ◽

Joonwhoan Lee

Keyword(s):

Real World ◽

Early Stage ◽

Pine Wilt Disease ◽

Wilt Disease ◽

Image Resolution ◽

Test Time ◽

Detection Model ◽

Pine Wilt ◽

Pine Trees ◽

Uav Images

Pine wilt is a devastating disease that typically kills affected pine trees within a few months. In this paper, we confront the problem of detecting pine wilt disease. In the image samples that have been used for pine wilt disease detection, there is high ambiguity due to poor image resolution and the presence of “disease-like” objects. We therefore created a new dataset using large-sized orthophotographs collected from 32 cities, 167 regions, and 6121 pine wilt disease hotspots in South Korea. In our system, pine wilt disease was detected in two stages: n the first stage, the disease and hard negative samples were collected using a convolutional neural network. Because the diseased areas varied in size and color, and as the disease manifests differently from the early stage to the late stage, hard negative samples were further categorized into six different classes to simplify the complexity of the dataset. Then, in the second stage, we used an object detection model to localize the disease and “disease-like” hard negative samples. We used several image augmentation methods to boost system performance and avoid overfitting. The test process was divided into two phases: a patch-based test and a real-world test. During the patch-based test, we used the test-time augmentation method to obtain the average prediction of our system across multiple augmented samples of data, and the prediction results showed a mean average precision of 89.44% in five-fold cross validation, thus representing an increase of around 5% over the alternative system. In the real-world test, we collected 10 orthophotographs in various resolutions and areas, and our system successfully detected 711 out of 730 potential disease spots.

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The specialization of Esteya vermicola hyphae in infection to Bursaphelenchus xylophilus and its colonization of pine tree

10.1101/2020.08.21.260729 ◽

2020 ◽

Author(s):

Hai-Hua Wang ◽

Can Yin ◽

Jie Gao ◽

Ran Tao ◽

Piao-Piao Dai ◽

...

Keyword(s):

Pine Wilt Disease ◽

Bursaphelenchus Xylophilus ◽

Wilt Disease ◽

Fungal Colonization ◽

Pinewood Nematode ◽

Pine Tree ◽

Pine Wilt ◽

Pine Trees ◽

Colonization Patterns ◽

Esteya Vermicola

AbstractPine wilt disease (PWD) caused by the nematode Bursaphelenchus xylophilus is a serious problem on pines, and there is currently no effective control strategy for this disease. Although the endoparasitic fungus Esteya vermicola showed great effectiveness in controlling pine wilt disease, the colonization patterns of the host pine tree xylem by this fungus are unknown. To investigate the colonization patterns of pine xylem by this fungus, the species Pinus koraiensis grown in a greenhouse was used as an experimental host tree. The fungal colonization of healthy and wilting pine trees by E. vermicola was quantified using PCR with a TaqMan probe, and a green fluorescence protein (GFP) transformant was used for visualization. The results reported a specific infection approach used by E. vermicola to infect B. xylophilus and specialized fungal parasitic cells in PWN infection. In addition, the inoculated blastospores of E. vermicola germinated and grew inside of healthy pine xylem, although the growth rate was slow. Moreover, E. vermicola extended into the pine xylem following spray inoculation of wounded pine seedling stems, and a significant increase in fungal quantity was observed in response to B. xylophilus invasion. An accelerated extension of E. vermicola colonization was shown in PWN-infected wilting pine trees, due to the immigration of fungal-infected PWNs. Our results provide helpful knowledge about the extension rate of this fungus in healthy and wilting PWN-susceptible pine trees in the biological control of PWD and will contribute to the development of a management method for PWD control in the field.Author summaryPine wilt disease, caused by Bursaphelenchus xylophilus, has infected most pine forests in Asian and European forests and led to enormous losses of forest ecosystem and economy. Esteya vermicola is a bio-control fungus against pinewood nematode, showed excellent control efficient to pine wilt disease in both of greenhouse experiments and field tests. Although this bio-control agent was well known for the management of pine wilt disease, the infection mechanism of fungal infection and colonization of host pine tree are less understand. Here, we use GFP-tagged mutant to investigate the fungal infection to pinewood nematode; additionally, the temporal and spatial dynamics of E. vermicola colonize to pine tree were determined by the TaqMan real-time PCR quantification, as well as the response to pinewood nematode invasion. We found a specific infection approach used by E. vermicola to infect B. xylophilus and specialized fungal parasitic cells in PWN infection. In addition, the fungal germination and extension inside of pine tree xylem after inoculation were revealed. In addition, the quantity of E. vermicola increased as response to pinewood nematode invasion was reported. Our study provides two novel technologies for the visualization and detection of E. vermicola for the future investigations of fungal colonization and its parasitism against pinewood nematode, and the mechanisms of the bio-control process.

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EXTRACTION OF THE INDIVIDUAL TREE INFECTED BY PINE WILT DISEASE USING UNMANNED AERIAL VEHICLE OPTICAL IMAGERY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2020-247-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 247-252

Author(s):

X. Zhou ◽

L. Liao ◽

D. Cheng ◽

X. Chen ◽

Q. Huang

Keyword(s):

Remote Sensing ◽

Southern China ◽

Vegetation Indices ◽

Pine Wilt Disease ◽

Field Measurements ◽

Wilt Disease ◽

Individual Tree ◽

Remote Sensing Technique ◽

Pine Wilt ◽

Pine Trees

Abstract. For eliminating pine trees infected pine wilt disease in southern China based on remote sensing technique, it is important to ensure the provision of timely information about individual diseased tree. It is not easy to detect and extract the diseased pine trees from conventional remote sensing techniques. This paper proposes a new approach for extracting information about individual diseased tree, without the use of satellite images and aerial hyperspectral images. Field measurements in different leaf infected stages indicates the possibility of extracting diseased trees by using only the three regular bands, red, green and blue. VEG was selected and proved to be the optimal index in 12 vegetation indices from the three visible bands. Using the adaptive local threshold selection methods, VEG grayscale image pixels could be automatically segmented into the diseased trees region. Based on mathematical morphology, the accuracy of individual tree information extraction reached 90%.

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The Microbiome of Endophytic, Wood Colonizing Bacteria from Pine Trees as Affected by Pine Wilt Disease

Scientific Reports ◽

10.1038/s41598-017-04141-6 ◽

2017 ◽

Vol 7 (1) ◽

Cited By ~ 24

Author(s):

Diogo Neves Proença ◽

Romeu Francisco ◽

Susanne Kublik ◽

Anne Schöler ◽

Gisle Vestergaard ◽

...

Keyword(s):

Pine Wilt Disease ◽

Wilt Disease ◽

Pine Wilt ◽

Pine Trees

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Volatile compounds in pine stands suffering from pine wilt disease: qualitative and quantitative evaluation

Nematology ◽

10.1163/156854106779799295 ◽

2006 ◽

Vol 8 (6) ◽

pp. 869-879 ◽

Cited By ~ 10

Author(s):

Kazuyoshi Futai ◽

Natsumi Kanzaki ◽

Yuko Takeuchi

Keyword(s):

Volatile Compounds ◽

Pine Wilt Disease ◽

Bursaphelenchus Xylophilus ◽

Wilt Disease ◽

Economic Damage ◽

Qualitative And Quantitative ◽

Pine Wilt ◽

Pine Trees ◽

Quantitative Properties ◽

Pine Stands

AbstractPine wilt disease causes ecological and economic damage in Japanese pine forests in spite of intensive effort to protect them from the pine wood nematode, Bursaphelenchus xylophilus. Pine trees infected with B. xylophilus emit a characteristic bouquet of volatile compounds bioactive to the vector beetle of the nematode, Monochamus alternatus, and potentially affecting symptom development inside the trees. To investigate the qualitative and quantitative properties of volatile compounds in the field, we profiled the volatile emissions in two Japanese black pine stands, one naturally suffering from pine wilt disease and the other artificially inoculated with B. xylophilus. In both pine stands, the emission of some terpenoids from the infected trees such as (−)-α-pinene, began to increase in summer, overlapping the oviposition season of the vector beetle, but peaked in the summer and autumn. These data suggest that the beetles may not necessarily depend on the tremendous quantity of volatiles alone when they search for suitable trees on which to oviposit.

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The Dark Septate Endophytes and Ectomycorrhizal Fungi Effect on Pinus tabulaeformis Carr. Seedling Growth and their Potential Effects to Pine Wilt Disease Resistance

Forests ◽

10.3390/f10020140 ◽

2019 ◽

Vol 10 (2) ◽

pp. 140 ◽

Cited By ~ 5

Author(s):

Honglong Chu ◽

Chuyan Wang ◽

Zhumei Li ◽

Haihua Wang ◽

Yuguo Xiao ◽

...

Keyword(s):

Ectomycorrhizal Fungi ◽

Growth Traits ◽

Pine Wilt Disease ◽

Wilt Disease ◽

Pinus Tabulaeformis ◽

Conifer Forest ◽

Cumulative Mortality ◽

Pine Wilt ◽

Pinus Tabulaeformis Carr ◽

The Relationship

Pine wilt disease (PWD), a worldwide threat to pine forests, has caused tremendous damage to conifer forest in the world. However, little research has been conducted on the relationship between symbiosis functions of root associated fungi and pine wilt disease. In this study, we assessed the influence of seven ectomycorrhizal fungi (ECMF) and five dark septate endophytic fungi (DSE) on the growth traits and root morphology as well as the correlation of these parameters to the cumulative mortality and the morbidity rates in Pinus tabulaeformis Carr.showed the lowest cumulative mortality rates. We propose that the ECMF/DSE symbiosis enhanced the resistance of pine wilt disease via mitigation the dysfunction of water caused by PWN infection. Our research provided evidence that inoculation of ECMF/DSE could be a potential way for pine wilt disease prevention. To find highly efficient fungi for pine wilt disease management, more ECMF and DSE species should be tested.

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