Genetic diversity and genome-wide association analysis of pine wood nematode populations in different regions of China DOI Creative Commons
Aixia Yang, Xiaolei Ding, Feng Yuan

et al.

Frontiers in Plant Science, Journal Year: 2023, Volume and Issue: 14

Published: June 23, 2023

Introduction Pine wilt disease ( Bursaphelenchus xylophilus ) was recently detected in Liaoning Province, which previously considered an unfavourable area for B. due to its low temperatures. This study aims compare the reproductivity and genetic variations of isolates from Province other parts China explore their phenotypic genomic differences. Methods The samples Liaoning, Anhui, Hubei, Henan, Zhejiang Jiangsu were isolated purified obtain strains. strains determined at 15 °C. structure analyzed by using SNP molecular markers, whole genome association analysis carried out integrating information feculence traits. Results A experiment showed that have higher reproductive ability Subsequent profiling population differentiation revealed obvious isolates. genome-wide SNPs closely related low-temperature tolerance mainly located GPCR, Acyl-CoA, Cpn10, are responsible adaptation environmental factors, such as temperature change. Discussion wood nematodes likely adapted climate maintained a certain capacity via variants adaptation-related genes. provides theoretical basis elucidating prevalence diffusion status China.

Language: Английский

Assessing the detectability of European spruce bark beetle green attack in multispectral drone images with high spatial- and temporal resolutions DOI Creative Commons
Langning Huo, Eva Lindberg, Jonas Bohlin

et al.

Remote Sensing of Environment, Journal Year: 2023, Volume and Issue: 287, P. 113484 - 113484

Published: Feb. 3, 2023

Detecting disease- or insect-infested forests as early possible is a classic application of remote sensing. Under conditions climate change and global warming, outbreaks the European spruce bark beetle (Ips typographus, L.) are threatening related timber industry across Europe, detection infestations important for damage control. Infested trees without visible discoloration (green attack) have been identified using multispectral images, but how green attacks can be detected still unknown. This study aimed to determine when infested start show an abnormal spectral response compared with healthy trees, quantify detectability during infestation process. Pheromone bags were used attract beetles in controlled experiment, subsequent assessed field on weekly basis. In total, 977 monitored, including 208 attacked trees. Multispectral drone images obtained before insect attacks, representing different periods infestation. Individual tree crowns (ITC) delineated by marker-controlled watershed segmentation, average reflectance ITCs was analyzed based duration The driving factors examined. We propose new Multiple Ratio Disease–Water Stress Indices (MR-DSWIs) vegetation indices (VI) detecting infestations. defined VI range 5–95% tree, value outside that tree. Detection rates always higher than observed field, newly proposed MR-DSWIs more established VIs. Infestations detectable at 5 10 weeks after attack rate 15% 90%, respectively, from images. Weeks 5–10 therefore represent suitable period methodology map stage.

Language: Английский

Citations

45
A review on machine learning and deep learning image-based plant disease classification for industrial farming systems DOI

P. Sajitha,

A. Diana Andrushia,

N. Anand

et al.

Journal of Industrial Information Integration, Journal Year: 2024, Volume and Issue: 38, P. 100572 - 100572

Published: Jan. 11, 2024

Language: Английский

Citations

45
Current advances in the identification of plant nematode diseases: From lab assays to in-field diagnostics DOI Creative Commons

Hudie Shao,

Pan Zhang,

Deliang Peng

et al.

Frontiers in Plant Science, Journal Year: 2023, Volume and Issue: 14

Published: Jan. 24, 2023

Plant parasitic nematodes (PPNs) cause an important class of diseases that occur in almost all types crops, seriously affecting yield and quality causing great economic losses. Accurate rapid diagnosis is the basis for their control. PPNs often have interspecific overlays large intraspecific variations morphology, therefore identification difficult based on morphological characters alone. Instead, molecular approaches been developed to complement morphology-based and/or avoid these issues with various degrees achievement. A number species successfully detected by biochemical techniques. Newly isothermal amplification technologies remote sensing methods recently introduced diagnose directly field. These useful because they are fast, accurate, cost-effective, but use integrative diagnosis, which combines methods, more appropriate In this paper, we review latest research advances status diagnostic techniques PPNs, goal improving detection.

Language: Английский

Citations

25
Transcriptomic, metabonomic and proteomic analyses reveal that terpenoids and flavonoids are required for Pinus koraiensis early defence against Bursaphelenchus xylophilus infection DOI Creative Commons

Lu Yu,

Yanna Wang, Xiang Wang

et al.

BMC Plant Biology, Journal Year: 2025, Volume and Issue: 25(1)

Published: Feb. 12, 2025

Abstract Pine wilt disease (PWD), caused by the pine wood nematode (PWN) Bursaphelenchus xylophilus , threatens Pinus seriously. koraiensis is one of most important species in China and host for PWN. However, our understanding defence-regulating process following infection B. at molecular level remains limited. To understand mechanisms that P. responds to invasion, was inoculated with solutions observed no obvious symptoms during early stage; began appear 5 dpi. Therefore, we conducted comparative transcriptomic, metabonomic proteomic analyses between 5dpi 0 In infected plants, 1574 genes were significantly up-regulated, including 17 terpenoid-, 41 phenylpropanoid- 22 flavonoid-related genes. According GO KEGG enrichment up-regulated genes, 86 terms 16 pathways enriched. Most associated phenylpropanoid-, flavonoid- carbohydrate-related events. Similarly, abundance 36 30 metabolites, increased positive negative polarity modes, respectively. Among them, naringenin 3-methyl-2-oxovaleric acid exhibited significant toxic effects on . functional analysis enriched above pathways, addition alkaloid biosynthesis. Although few proteins changed, response stress term proteins. Furthermore, plant receptor-like serine/threonine kinases, pectin methylation modulators, pinosylvin O-methyltransferase arabinogalactan/proline-rich compared healthy plants. These not abundant plant. Overall, these results indicate can actively PWN via various defense strategies, events related terpenoids, flavonoids, phenylpropanoids, lipids alkaloids. Particularly, terpenoids flavonoids are required defence against infection.

Language: Английский

Citations

1
Rapid Spread and High Prevalence of the Pine Wilt Disease around Wildfire Areas DOI Creative Commons
Tae‐Hoon Lee, Jee-Young Kim

Trees Forests and People, Journal Year: 2025, Volume and Issue: unknown, P. 100805 - 100805

Published: Feb. 1, 2025

Language: Английский

Citations

1
Development of a 101.6K liquid‐phased probe for GWAS and genomic selection in pine wilt disease‐resistance breeding in Masson pine DOI Creative Commons
Jing-qiu Zhu, Qinghua Liu, Shu Diao

et al.

The Plant Genome, Journal Year: 2025, Volume and Issue: 18(1)

Published: March 1, 2025

Masson pine (Pinus massoniana Lamb.), indigenous to southern China, faces serious threats from wilt disease (PWD). Several natural genotypes have survived PWD outbreaks. Conducting genetic breeding with these resistant holds promise for enhancing resistance in at its source. We conducted a genome-wide association study (GWAS) and genomic selection (GS) on 1013 seedlings 72 half-sib families advance disease-resistance breeding. A set of efficient 101.6K liquid-phased probes was developed single-nucleotide polymorphisms (SNPs) genotyping through target sequencing. inoculation experiments were then performed obtain phenotypic data populations. Our analysis reveals that the targeted sequencing successfully divided experimental population into three subpopulations consistent provenance, verifying reliability probe. total 548 SNPs considerably associated traits using four GWAS algorithms. Among them, 283 located or linked 169 genes, including common plant resistance-related protein such as NBS-LRR AP2/ERF. The DNNGP (deep neural network-based method prediction) model demonstrated superior performance GS, achieving maximum predictive accuracy 0.71. predictions reached 90% top 20% testing ordered by estimated value. This establishes foundational framework advancing research disease-resistant genes P. offers preliminary evidence supporting feasibility utilizing GS early identification individuals.

Language: Английский

Citations

1
Detection Methods for Pine Wilt Disease: A Comprehensive Review DOI Creative Commons

Sana Tahir,

Sher Hassan,

Lu Yang

et al.

Plants, Journal Year: 2024, Volume and Issue: 13(20), P. 2876 - 2876

Published: Oct. 14, 2024

Pine wilt disease (PWD), caused by the nematode

Language: Английский

Citations

6
Early-Stage Pine Wilt Disease Detection via Multi-Feature Fusion in UAV Imagery DOI Open Access
Wanying Xie, Han Wang, Wenping Liu

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(1), P. 171 - 171

Published: Jan. 14, 2024

Pine wilt disease (PWD) is a highly contagious and devastating forest disease. The timely detection of pine trees infected with PWD in the early stage great significance to effectively control spread protect resources. However, spatial domain, features early-stage are not distinctly evident, leading numerous missed detections false positives when directly using spatial-domain images. we found that frequency domain information can more clearly express characteristics PWD. In this paper, propose method based on deep learning for by comprehensively utilizing domain. An attention mechanism introduced further enhance features. Employing two deformable convolutions fuse both domains, aim fully capture semantic information. To substantiate proposed method, study employs UAVs images at Dahuofang Experimental Forest Fushun, Liaoning Province. A dataset affected curated facilitate future research infestations trees. results indicate that, compared Faster R-CNN, DETR YOLOv5, best-performing improves average precision (AP) 17.7%, 6.2% 6.0%, F1 scores 14.6%, 3.9% 5.0%, respectively. provides technical support tree counting localization field areas lays foundation wood nematode

Language: Английский

Citations

5
Harnessing synthetic data for enhanced detection of Pine Wilt Disease: An image classification approach DOI
Yong-Hoon Jung,

Sanghyun Byun,

Bumsoo Kim

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 218, P. 108690 - 108690

Published: Feb. 2, 2024

Language: Английский

Citations

5
Detection of Pine-Wilt-Disease-Affected Trees Based on Improved YOLO v7 DOI Open Access
Xianhao Zhu, Ruirui Wang, Wei Shi

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(4), P. 691 - 691

Published: April 11, 2024

Pine wilt disease (PWD) poses a significant threat to global pine resources because of its rapid spread and management challenges. This study uses high-resolution helicopter imagery the deep learning model You Only Look Once version 7 (YOLO v7) detect symptomatic trees in forests. Attention mechanism technology from artificial intelligence is integrated into enhance accuracy. Comparative analysis indicates that YOLO v7-SE exhibited best performance, with precision rate 0.9281, recall 0.8958, an F1 score 0.9117. demonstrates efficient precise automatic detection forest areas, providing reliable support for prevention control efforts, emphasizes importance attention mechanisms improving performance.

Language: Английский

Citations

4