Diseased Trees Research Articles

Predictive Health Monitoring of Palm Trees: Techniques and Applications for Enhanced Processing

This paper presents new framework for predictive health monitoring in palm trees using advanced deep learning methods. The system then selects the YOLOv8 method for the detection of the health of palm trees and further divides them into their real-time classification. Here, the model will, after training on its dataset, classify the palm tree as healthy, diseased, and stressed trees, keeping all the influential factors of the growth of the palm tree in consideration. It makes provision for the profiling of health status by way of early intervention, reducing risks and preventing diseases with a view to optimizing crop yields within palm plantations. The proposed model leverages state-of-the-art methodologies in object detection to process images of the palm tree with identification of key indicators of health issues. This approach has great implications for agricultural productivity in view of the maintenance of plant health through early detection. Further, the paper discusses challenges presented during the training and validation of the model, their strategies for overcoming such obstacles, and goes further into relating details to the technical architecture of the model. Further details on the works related to plant disease detection, image-based classification, and further applications of deep learning in agriculture have inspired the research work. Based on the obtained results, it can be observed that the proposed system provides a scalable and efficient solution for continuous monitoring and assessment of the health conditions of palm trees, hence enabling sustainable agricultural practices.

Distribution patterns of Calonectria (Ascomycota, Sordariomycetes, Hypocreales, Nectriaceae) species complexes related to diseased leaves and soil habitats during leaf blight outbreak season in Eucalyptus plantations in southern China.

Calonectria leaf blight caused by Calonectria species is one of the most important diseases associated with Eucalyptus plantations in Asia and South America. This study aimed to clarify the distribution patterns of Calonectria species residing in different species complexes associated with diseased trees and soils during leaf blight outbreak season in Eucalyptus plantations in southern China. In this study, 482 Calonectria isolates obtained from diseased Eucalyptus trees and soils under these trees in eight sampling sites in three provinces were identified by DNA sequence analyses of tef1, tub2, cmdA, and his3 gene regions. Six species residing in three species complexes were identified: Calonectriapseudoreteaudii and C.acaciicola in the Calonectriareteaudii species complex; C.hongkongensis, C.aconidialis, and C.chinensis in C.kyotensis species complex; and C.auriculiformis in C.cylindrospora species complex. The habitats of Calonectria in different species complexes differed, C.reteaudii species complex inhabits in both diseased trees and soils, C.kyotensis species complex only in soils. The Calonectria leaf blight in the sampled regions was caused by species in the C.reteaudii species complex but not by the species in the C.kyotensis species complex. These findings suggest that the species in the C.reteaudii species complex should receive more attention in disease management, as they are the primary cause of the disease in the sampled regions.

Botryosphaeriaceae Infections on Prunus Germplasm: Evaluation of Pathogenicity, Resistance Loci, and Detached Assays of Southeastern United States Isolates

Research of peach fungal gummosis (PFG), which is a vascular disease caused by fungi in the Botryosphaeriaceae family, is particularly important in the southeastern United States, southern China, South Africa, and Western Australia. This disease has been a significant concern for the peach industry in the southeastern United States since the 1970s. The changing climatic conditions in the prunus production areas of the world have worsened the severity of PFG disease. A study of the morphological and genetic profiles of Botryosphaeriaceae species from diseased peach trees found four species in Georgia, Florida, Alabama, and South Carolina. Botryosphaeria dothidea, Lasiodiplodia theobromae, Diplodia seriata, and Neofusicocoum parvum were isolated and identified using conidiospore characters and rDNA sequences of internal transcribed spacer regions and elongation factor α-1 genes. B. dothidea was the most common species from diseased peach trees. A significant implication of this research is the potential resistance in Prunus germplasm, which was genotyped for the resistance locus Botd8 and evaluated for relative susceptibility to one isolate each from B. dothidea, L. theobromae, and D. seriata. Pathogenicity evaluations of peach, almond, and interspecific hybrids used detached stem and leaf assays for susceptibility to PFG based on lesion lengths and gumming scores. L. theobromae inoculation resulted in the most extensive lesions on stems and leaves. Detached stem and leaf assays indicated that Prunus with the Botd8 locus significantly differed in lesion size when infected with B. dothidea and D. seriata. However, the resistant locus had no significant effect on the relative susceptibility of Prunus to L. theobromae. Field and detached stem and leaf assays identified almond cultivars Pioneer and Golden State and Peach × Almond hybrids with Botd8 (+) as tolerant to PFG. Correlations between detached assays and field gumming scores suggested that inoculations on detached stems or leaves under controlled conditions can be used to preliminarily screen for resistance to PFG. These detached assays for specific pathogen isolates provide an opportunity for a relatively fast, nondestructive evaluation of Prunus germplasm and are tools that can be used to identify the components of disease resistance.

Comparison of deep learning-based models for detection of diseased trees using an image compression algorithm

The object of the research is the application of deep learning algorithms using an improved mathematical lossless image compression method for recognizing and identifying dead trees in aerospace images. The main problem that has been solved is the archiving of images due to their large volume on disk and the possibility of their further processing by deep learning methods such as convolutional and capsule neural networks, which have shown high efficiency and accuracy in image recognition and classification tasks using the proposed new image compression method. The article presents a comparative analysis of the performance of three YOLO (You Only Look Once) models with different types of architectures, such as YOLOv5, YOLOv7 and YOLOv8, to assess the effectiveness of their work for the task of recognizing aerospace tree images obtained from satellites, drones, and aircrafts. Comprehensive analysis of YOLO models presents that model YOLO v8 turned out to be most effective with a positive accuracy of 88.2 %, a recall of 77.4 %, and a mAP50 score of 87.2 %. Moreover, the average detection time was only 0.052 seconds for each image, even though the model size remains very small – 21.5 MB. These results suggest a much better usage of time and precise identification of dead trees, and classified targets with high efficiency. From the research, there is significant prospects of global forest management especially on forest reduction and protection of ecosystems through accurate assessment on the health of forestry. The proposed approach is universal and can be used in real life conditions, providing a good compromise of the speed, accuracy and resources required for forest monitoring and management

The epidemic occurrence of decline disease in bayberry trees altered plant and soil related microbiome and metabolome

BackgroundIn China, decline disease with unknown etiology appeared as an epidemic among bayberry trees in the southern area of the Yangtze River. Furthermore, the use of beneficial microbes has been reported to be able to reduce the incidence of this disease, emphasizing the association of this disease with microorganisms. Therefore, it has become critical to uncover the microbiome’s function and related metabolites in remodeling the immunity of bayberry trees under biotic or abiotic stresses.ResultsThe amplicon sequencing data revealed that decline disease significantly altered bacterial and fungal communities, and their metabolites in the four distinct niches, especially in the rhizosphere soils and roots. Furthermore, the microbial communities in the four niches correlated with the metabolites of the corresponding niches of bayberry plants, and the fungal and bacterial networks of healthy trees were shown to be more complex than those of diseased trees. In addition, the role of microbiome in the resistance of bayberry trees to the occurrence of decline disease was justified by the isolation, identification, and characterization of important microorganisms such as significantly enriched Bacillus ASV804, Pseudomonas ASV815 in healthy plants, and significantly enriched Stenotrophomonas ASV719 in diseased plants.ConclusionOverall, our study revealed that the occurrence of decline disease altered the microbiome and its metabolites in four ecological niches in particular rhizosphere soils and roots of bayberry, which provides new insight into the control of bayberry decline disease.

Opportunities for the Early Diagnosis and Selection of Scots Pine with Potential Resistance to Root and Butt Rot Disease

Pine stands affected by root and butt rot (Heterobasidion annosum s.l.) contain pines (Pinus sylvestris L.) that can survive for a long time without showing external symptoms of the disease (‘conditionally resistant’ refers to trees that survive without symptoms despite infection). The establishment of stands from the seeds of such trees can significantly increase the effectiveness of artificial afforestation. Since the growth and development of pine trees is determined to a certain extent by the number of cotyledons after seed germination, this article examines this trait in the progeny of trees that are potentially resistant and those that have already been attacked by root pathogens. The number of cotyledons and the resilience of trees is fascinating and not generally known. Presumably, the number of cotyledons can be linked to disease resistance based on increased vigour. Biologically, a larger area for carbon assimilation leads to better photosynthetic efficiency and the production of more assimilates (sugars) necessary to trigger defence processes in the event of infection. From an ecological point of view, this can give tree populations in areas potentially threatened by root system diseases a chance of survival. The aim of this study was to analyze the potential of using the number of cotyledons and other seedling characteristics to predict the resistance of trees to root and butt rot disease. The collected data show that the seedlings from the group of diseased trees exhibited lower growth rates and vigour. However, the seedlings from the group of potentially resistant trees are similar to the control, meaning the trees that show no disease symptoms because they have not come into contact with the pathogen. Our observations suggest that monitoring germinating cotyledons could serve as an early diagnostic tool to identify disease-resistant pines, although further research is needed.

Comparison of Deep Learning Models and Feature Schemes for Detecting Pine Wilt Diseased Trees

Comparison of Deep Learning Models and Feature Schemes for Detecting Pine Wilt Diseased Trees

Identification of fungal species associated with gall oak (Quercus infectoria Oliv.) decline in Iran.

The gall oak (Quercus infectoria Oliv.) tree is one of the most important and valuable forestry species in the Northern Zagros forests in the west of Iran. Gall oak decline is considered to be one of the most important diseases currently affecting the oak Zagros forests in Iran. The main objective of the present study, conducted in the years 2021-2023, was to investigate the possible role of fungi as causative agents of gall oak dieback in the Zagros forests of Iran. Wood samples were taken from gall oak trees showing canker, dieback, and internal wood discoloration symptoms. Fungal isolates recovered from gall oak trees were identified based on cultural and morphological characteristics, as well as phylogenetic analyses using DNA sequencing of the internal transcribed spacer region of rDNA (ITS) and partial beta-tubulin (tub2). Achaetomium aegilopis, Alternaria tenuissima, Apiospora intestine, Botrytis cinerea, Coniochaeta sp., Coniothyrium palmarum, Coniothyrium sp., Cytospora rhodophila, Dialonectria episphaeria, Diatrype sp., Diatrypella macrospora, Endoconidioma populi, Fonsecazyma sp., Fusarium ipomoeae, Jattaea discrete, Kalmusia variispora, Microsphaeropsis olivacea, Neoscytalidium dimidiatum, Paecilomyces lecytidis, Paramicrosphaeropsis eriobotryae, Paramicrosphaeropsis ellipsoidea, and Seimatosporium pezizoides were identified from diseased trees. Pathogenicity tests were performed by artificial inoculation of excised branches of healthy gall oak trees under controlled conditions and evaluated after 35 days by measuring the discolored lesion length at the inoculation site. N. dimidiatum was the most virulent species and caused the longest wood necrosis within 35 days of inoculation. In the greenhouse test, only some species induced typical symptoms of canker. All isolated fungi are reported for the first time on gall oak trees in the world.

Comparative Analysis of Microbial Communities in Diseased and Healthy Sweet Cherry Trees (Prunus avium L.).

The European sweet cherry Prunus avium (L.), a member of the Rosaceae family, is one of the most popular and economically valuable fruits. However, the rapid spread of gummosis and poor management practices have become the major obstacles to their production. To identify pathogenic microorganisms responsible for gummosis disease, we conducted observations comparing the garden of Bailuyuan, which heavily suffered from gummosis disease and horn beetle damage, with the orchard of Mayuhe, which only suffered from gummosis disease, both from Xi'an, Shaanxi, China. Samples were obtained from the healthy tissues and gummosis disease tissues that used the Illumina sequence of 16S rRNA and the internal transcribed spacer region (ITS) to identify bacterial and fungal communities in these samples. An alpha diversity analysis revealed a significantly higher fungal diversity of disease than in healthy tissue in the gummosis period. The results suggested that an imbalance in the fungal genera may be associated with gummosis disease. Species relative analyses showed some bacterial genera (Pelagibacterium, Halomonas, Azospirillum, Aquabacterium and Alistipes) and fungal genera (Penicillium, Alternaria and Rhodotorula) in the diseased tissues of gummosis. Among these, the increased relative abundance of the bacteria genes Halomonas, Pelagibacterium, Chelativorans, Pantoea, Aquabacterium, Alternaria and fungi genes Penicillium, Cystobasidium, Rhodotorula may be associated with gummosis of P. avium. The bacterial genera Methylobacterium, Psychroglaciecola, Aeromonas, Conexibacter and fungal genera Didymella, Aureobasidium, Mycosphaerella, Meyerozyma are probably antagonists of the pathogen of gummosis. These findings are an initial step in the identification of potential candidates for the biological control of the disease.

Isolation, Identification, and Biocontrol Mechanisms of Endophytic Burkholderia arboris DHR18 from Rubber Tree against Red Root Rot Disease.

Red root rot disease is a devastating fungal disease of rubber trees caused by Ganoderma pseudoferreum (Wakef). Biocontrols using beneficial microorganisms are safe and sustainable. We isolated a DHR18 endophytic bacterium from a healthy rubber tree to obtain a new efficient antagonistic bacterium for red root rot disease affecting rubber trees and evaluated the mechanism of action involved using a double culture assay, genome annotation analysis, and the ethyl acetate extraction method. The results revealed that the DHR18 strain inhibits G. pseudoferreum growth and has broad-spectrum antifungal activity by secreting cell wall hydrolases (proteases and chitinases), indole-3-acetic acid, and siderophores. Furthermore, it fixes nitrogen and is involved in biofilm formation and phosphate solubilisation, improving disease resistance and tree growth. The results showed that the antifungal substances secreted by DHR18 are mainly lipopeptides. Simultaneously, DHR18 enhanced the rubber tree resistance to G. pseudoferreum by increasing the activities of defence enzymes superoxide dismutase, phenylalanine ammonia lyase, peroxidase, catalase, and polyphenol oxidase. The results indicate that B. arboris DHR18 has biocontrol potential and could be used as a candidate strain for the control of red root rot disease in rubber trees.

First Report of Colletotrichum fioriniae Causing Anthracnose on Persimmon in China.

Persimmons (Diospyros kaki Thunb.) have a longstanding history of cultivation in China. Both aesthetically pleasing and edible, they often symbolize a sweet and fulfilling life. During the summer of 2022, a severe outbreak of anthracnose was observed on the lower leaves of persimmon trees in the National Field Genebank for Persimmon (NFGP), located in Yangling, Shaanxi, China (34°17'42.80″ N, 108°04'08.21″ E). The estimated incidence rate of this disease within the NFGP was approximately 30%. The typical symptoms of the disease included the presence of irregular lesions on leaves, and oval sunken lesions on infected fruit. Under high humidity conditions, pink sticky substances appeared in the affected areas. The presence of numerous lesions led to softening and detachment of persimmon fruit. To identify the causal pathogen, 5 × 5mm samples of the diseased leaves were collected from the interface between the infected and healthy leaves. The leaves were disinfected with 70% alcohol for 20 s, followed by rinsing with sterile water. Subsequently, the leaves were immersed in 1% NaClO for 2 to 3 minutes, rinsed with sterile water three times, dried using sterile absorbent paper, and the leaf samples were then transferred onto potato dextrose agar (PDA) medium, and cultured in 25°C incubators. Once the colony reached a certain size, small pieces of hyphae were extracted from edge and transferred for purification and repeated three times. After being cultured on PDA for 7 days, the colony showed a white spongy surface with a pink-orange center. The conidia displayed a fusiform shape and were transparent, measuring 4.58 to 6.53 μm × 9.27 to 13.11 μm (n=50). The isolates share morphological similarities with Colletotrichum fioriniae. The representative isolate HY-7 was selected for molecular identification. The internal transcribed spacers (ITS) region, chitin synthase (CHS-1), actin (ACT), beta-tubulin 2 (TUB2), and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) gene were amplified using ITS1/4 (White et al. 1990), CHS-79F/CHS-345R (Carbone & Kohn, 1999), ACT512F/ACT (Carbone & Kohn, 1999), T1/BT2B (Glass & Donaldson 1995, O'Donnell et al., 1997), and GDF/GDR (Templeton et al. 1992), respectively. The generated sequences were deposited at GenBank under accession numbers OR878056 (ITS), OR766019 (CHS-1), OR766021(TUB2), OR766018 (ACT) and OR766020 (GAPDH). BLAST analysis revealed the sequences were 100% identical to C. fioriniae (MH865005 for ITS, JQ948953 for CHS-1, JQ949613 for ACT, JQ949943 for TUB2 and JQ948622 for GAPDH). The morphological characteristics and molecular analyses of the isolate matched the description of C. fioriniae. To fulfill Koch's postulates, the twigs and leaves of 'Fupingjianshi' in four different directions were inoculated without wounding in the field, and 10 healthy fruits were selected for wound inoculation. The concentration of conidia used for inoculation was about 1 × 106 conidia/ml, and sterilized water was used as control. The experiment was replicated three times under the same conditions. One week after inoculation, characteristic symptoms resembling those observed on the leaves of primary diseased persimmon trees appeared on the leaves and fruits. No symptoms were observed on the leaves, twigs and fruits in the control treatment. The pathogen from the artificially infected leaves and fruits were reisolated and identified as C. fiorinae based on morphological and molecular characteristics. Persimmon anthracnose is a common disease in regions where the fruit is grown, to the best of our knowledge, this is the first documented occurrence of C. fioriniae-induced anthracnose on persimmons in China, which should be paid more attentions. This report will help identify disease symptoms in the field and provides a basis for determining the occurrence, distribution, and control of C. fioriniae on persimmon leaves and fruits.

Identification of Pine Wilt-Diseased Trees Using UAV Remote Sensing Imagery and Improved PWD-YOLOv8n Algorithm

Pine wilt disease (PWD) is one of the most destructive diseases for pine trees, causing a significant effect on ecological resources. The identification of PWD-infected trees is an effective approach for disease control. However, the effects of complex environments and the multi-scale features of PWD trees hinder detection performance. To address these issues, this study proposes a detection model based on PWD-YOLOv8 by utilizing aerial images. In particular, the coordinate attention (CA) and convolutional block attention module (CBAM) mechanisms are combined with YOLOv8 to enhance feature extraction. The bidirectional feature pyramid network (BiFPN) structure is used to strengthen feature fusion and recognition capability for small-scale diseased trees. Meanwhile, the lightweight FasterBlock structure and efficient multi-scale attention (EMA) mechanism are employed to optimize the C2f module. In addition, the Inner-SIoU loss function is introduced to seamlessly improve model accuracy and reduce missing rates. The experiment showed that the proposed PWD-YOLOv8n algorithm outperformed conventional target-detection models on the validation set (mAP@0.5 = 94.3%, precision = 87.9%, recall = 87.0%, missing rate = 6.6%; model size = 4.8 MB). Therefore, the proposed PWD-YOLOv8n model demonstrates significant superiority in diseased-tree detection. It not only enhances detection efficiency and accuracy but also provides important technical support for forest disease control and prevention.

Spatial Pattern of Host Tree Size, Rather than of Host Tree Itself, Affects the Infection Likelihood of a Fungal Stem Disease.

The spatial pattern of diseased forest trees is a product of the spatial pattern of host trees and the disease itself. Previous studies have focused on describing the spatial pattern of diseased host trees, and it remains largely unknown whether an antecedent spatial pattern of host/nonhost trees affects the infection pattern of a disease and how large the effect sizes of the spatial pattern of host/nonhost trees and host size are. The results from trivariate random labeling showed that the antecedent pattern of the host ash tree, Fraxinus mandshurica, but not of nonhost tree species, impacted the infection pattern of a stem fungal disease caused by Inonotus hispidus. To investigate the effect size of the spatial pattern of ash trees, we employed the SADIE (Spatial Analysis by Distance IndicEs) aggregation index and clustering index as predictors in the GLMs. Globally, the spatial pattern (vi index) of ash trees did not affect the infection likelihood of the focal tree; however, the spatial pattern of DBH (diameter at breast height) of ash trees significantly affected the infection likelihood of the focal tree. We sampled a series of circular plots with different radii to investigate the spatial pattern effect of host size on the infection likelihood of the focal tree locally. The results showed that the location (patch/gap) of the DBH of the focal tree, rather than that of the focal tree itself, significantly affected its infection likelihood in most plots of the investigated sizes. A meta-analysis was employed to settle the discrepancy between plots of different sizes, which led to results consistent with those of global studies. The results from meta-regression showed that plot size had no significant effects.

Diversity of Calonectria species from leaves and soils in diseased southern China Eucalyptus plantation

Calonectria leaf blight (CLB) is one of the best-known diseases of Eucalyptus spp., particularly in Asia and South America. Recently, typical symptoms of leaf and shoot blight caused by Calonectria spp. Were observed in a Eucalyptus plantation in the YunNan Province of southwestern China. Isolations were made from diseased leaves and top soil collected below the diseased trees to determine the causal agent of the disease and to consider the distribution characteristics of the Calonectria species. This resulted in 417 isolates, of which 228 were from leaves and 189 were from soil. Based on comparisons of DNA sequences for the act (actin), cmdA (calmodulin), his3 (histone H3), rpb2 (the second largest subunit of RNA polymerase), tef1 (translation elongation factor 1-alpha) and tub2 (β-tubulin) gene regions, as well as morphological characteristics, 11 Calonectria species were identified. These included Calonectria aciculata (0.7 %), Ca. colhounii (1.2 %), Ca. eucalypti (10.6 %) and Ca. honghensis (43.2 %) in the Ca. colhounii species complex, and Ca. aconidialis (15.3 %), Ca. asiatica (9.8 %), Ca. hongkongensis (1.0 %), Ca. ilicicola (6.0 %), Ca. kyotensis (0.5 %), and Ca. yunnanensis (11.3 %) in the Ca. kyotensis species complex. In addition, a novel species, accounting for 0.5 % of the isolates, was discovered and is described here as Ca. dianii sp. nov. in the Ca colhounii species complex. Most (99.1 %) of the isolates collected from the leaves resided in the Ca. colhounii species complex and a majority (95.8 %) of those from the soils were in Ca. kyotensis species complex. These results suggest that Calonectria spp. in the Ca. colhounii species complex infecting leaves might be adapted to that niche and that those in the Ca. kyotensis species complex are better adapted to a soil habitat.

A spatio-temporal multi-scale fusion algorithm for pine wood nematode disease tree detection

Pine wood nematode infection is a devastating disease. Unmanned aerial vehicle (UAV) remote sensing enables timely and precise monitoring. However, UAV aerial images are challenged by small target size and complex surface backgrounds which hinder their effectiveness in monitoring. To address these challenges, based on the analysis and optimization of UAV remote sensing images, this study developed a spatio-temporal multi-scale fusion algorithm for disease detection. The multi-head, self-attention mechanism is incorporated to address the issue of excessive features generated by complex surface backgrounds in UAV images. This enables adaptive feature control to suppress redundant information and boost the model’s feature extraction capabilities. The SPD-Conv module was introduced to address the problem of loss of small target feature information during feature extraction, enhancing the preservation of key features. Additionally, the gather-and-distribute mechanism was implemented to augment the model’s multi-scale feature fusion capacity, preventing the loss of local details during fusion and enriching small target feature information. This study established a dataset of pine wood nematode disease in the Huangshan area using DJI (DJ-Innovations) UAVs. The results show that the accuracy of the proposed model with spatio-temporal multi-scale fusion reached 78.5%, 6.6% higher than that of the benchmark model. Building upon the timeliness and flexibility of UAV remote sensing, the proposed model effectively addressed the challenges of detecting small and medium-size targets in complex backgrounds, thereby enhancing the detection efficiency for pine wood nematode disease. This facilitates early preemptive preservation of diseased trees, augments the overall monitoring proficiency of pine wood nematode diseases, and supplies technical aid for proficient monitoring.

First Report of Leaf Brown Spot Caused by Diaporthe phoenicicola on Pachira glabra in China.

Pachira glabra is an increasingly important ornamental landscape tree in southern China. In August 2022, brown spots were observed on P. glabra leaves in Xiangtan City, Hunan Province, China (27.932°N, 113.020°E), affecting up to 40% of the 792 trees surveyed. On each diseased tree, nearly 30% leaves had symptoms, with an average severity of 21.2 ± 5.8% (n=100). The disease initially started as small yellow lesions along leaf margins, which later progressed to pale brown to brown with dark brown borders, eventually coalescing into large necrotic areas. Thirty symptomatic leaf samples (2 × 2 mm) were surfaced-sterilized in 75% ethanol for 10 s, 2% NaOCl for 30 s, rinsed in sterile water three times, placed on potato dextrose agar (PDA), and incubated at 25°C for 5 to 7 days in dark. Eight morphologically similar isolates were obtained from diseased leaf samples through single-spore isolation. On PDA, colonies initially appeared white, turning gray, while the reverse developed a pale yellowish hue. Aerial mycelia were white, cottony, and developed visible black pycnidia with oil droplets at maturity. The α-conidia were unicellular, hyaline, aseptate, oval or fusiform, usually with 1 or 2 guttule(s) and rounded at each end. These conidia were 5.3-8.6 × 1.7-2.5 μm (avg. 6.7 × 2.2 μm, n = 100) and present more frequently than β-conidia.The β-conidia were unicellular, hyaline, aseptate, filiform, straight or hamate, eguttulate, 14.6-23.3 × 0.4-1.3 μm (avg. 18.4 × 0.9 μm, n = 30). Morphologically, the fungi were identified as Diaporthe sp. (Udayanga et al. 2014). For molecular identification, the internal transcribed spacer region (ITS), translation elongation factor 1α (EF1-α), calmodulin (CAL), tubulin 2 (TUB2), and histone H3 (HIS3) sequences of all isolates were amplified from genomic DNA, using primers ITS4/ITS5 (White et al. 1990), TEF-2/728F and CALD-38F/CALD-752R (Carbone and Kohn 1999), Bt2a/Bt2b and H3-1a/H3-1b (Glass and Donaldson 1995; Crous et al. 2004), respectively. The GenBank accession numbers for a representative isolate gpg2023-1 were OR533573 (ITS), OR570887 (EF1-α), OR570888 (TUB2), OR570890 (CAL), and OR570889 (HIS3). BLAST results showed that the ITS, EF1-α, TUB2, HIS, and CAL sequences were 99%, 99%, 99%, 99%, and 98% identity, respectively, with those of Diaporthe phoenicicola (GenBank: KC343032.1, KC343758.1, KC344000.1, KC343516.1, and KC343274.1). To confirm the pathogen's identity, phylogenetic analysis using MEGA7.0 based on Maximum Likelihood was constructed. Isolate gpg2023-1 clustered with D. phoenicicola. Based on morphological and molecular data, the fungus was identified as D. phoenicicola. Next, pathogenicity tests were performed three times on one-year-old potted P. glabra plants. For each isolate, twelve healthy leaves on each of three plants were either wounded by a sterile needle or left unwounded, and then sprayed with a conidial suspension (1×106 conidia/ml) for each isolate. Control plants received with sterile water only. Plants were kept in a greenhouse at 25°C, 80% relative humidity, with a 12-h photoperiod. All wounded, inoculated leaves developed brown spot symptoms similar to those observed in the field with six days, while unwounded leaves and control plants remained symptom-free. The fungus was reisolated from all diseased leaves, fulfilling Koch's postulates and proving D. phoenicicola as the causative agent of this brown spot disease on P. glabra. While D. pachirae has been reported to cause leaf spot on P. glabra in Brazil (Milagres et al. 2018), this study marks the first report of D. phoenicicola causing leaf brown spot on P. glabra in China. This finding can help develop control strategies for this disease.

Emergence Characteristics of Fire Blight from 2019 to 2023 in Korea

Erwinia amylovora is a gram-negative plant pathogen that causes fire blight in apple and pear trees, resulting in significant damage worldwide. In this study, we monitored the emergence of fire blight from 2019 to 2023 to determine the emergence patterns and the factors affecting the outbreak of the disease. As a result of the 5-year survey on the emergence of fire blight, a total of 2,029 cases have emerged, mostly in apple trees of 1,378 cases (67.9%) followed by 645 cases (31.8%) in pear trees, and from quince, hawthorn, and mountain ash trees. Fire blight appeared in specific areas of Gyeonggi, Chungnam, Gangwon, and Chungbuk provinces in 2019, but spread to Andong and Yesan in 2021, Muju and Bonghwa in 2023. In 2020 and 2021, there were 744 and 618 cases of fire blight outbreaks, respectively, compared to other years (188–245 cases/year). Notably, 914 of these cases were observed in apple trees from May to July, with 667 cases reported in Chungju and Jecheon. The incidence of fire blight was positively correlated with the daily maximum temperatures and rainy days in January and February, as well as the rainy days in May and June. The average age of the diseased pear trees was 25 years, higher than the 10-year average age of the apple trees. This study provides fundamental information to understand the status and factors affecting the fire blight emergence in Korea. Prevention measures should be established through continuous analysis of the status of fire blight.

Detection of huanglongbing infection in citrus using compositional analysis of volatile organic compounds

AbstractHuanglongbing (HLB) is caused by “Candidatus Liberibacter asiaticus” and is spread by citrus psyllids. It is a highly damaging and infectious disease of citrus for which there is no reliable treatment. Timely detection and removal of diseased trees is an effective strategy to control HLB. Various citrus cultivars infected with HLB exhibit distinct symptoms, posing a challenge to generalize HLB detection methods across multiple cultivars. Volatile organic compounds (VOCs) in HLB‐infected leaves from cultivars grown in different regions were comprehensively analysed by gas chromatography–mass spectrometry (GC‐MS) and gas chromatography‐ion mobility spectrometry (GC‐IMS), aiming to establish a new generalized HLB detection method applicable to a range of citrus cultivars. The changes in VOCs were analysed in leaves of four citrus cultivars after infection with HLB. The results showed that there was a similar response to HLB infection in different citrus cultivars, and this response was reflected in both the concentration and type of VOCs. By combining GC‐MS and GC‐IMS with orthogonal partial least squares discriminant analysis (OPLS‐DA) model, the accurate identification of the HLB infection status of different citrus cultivars was achieved, with the prediction indices reaching 0.994 for GC‐MS and 0.972 for GC‐IMS. In addition, 27 compounds were identified that were significantly different between citrus cultivars affected by HLB and healthy plants. This study provides valuable insights into the changes in VOCs in citrus cultivars after HLB infection and lays the theoretical foundation for VOC‐based HLB detection strategies.

A Lightweight Pine Wilt Disease Detection Method Based on Vision Transformer-Enhanced YOLO

Pine wilt disease (PWD) is a forest disease characterized by rapid spread and extremely high lethality, posing a serious threat to the ecological security of China’s forests and causing significant economic losses in forestry. Given the extensive forestry area, limited personnel for inspection and monitoring, and high costs, utilizing UAV-based remote sensing monitoring for diseased trees represents an effective approach for controlling the spread of PWD. However, due to the small target size and uneven scale of pine wilt disease, as well as the limitations of real-time detection by drones, traditional disease tree detection algorithms based on RGB remote sensing images do not achieve an optimal balance among accuracy, detection speed, and model complexity due to real-time detection limitations. Consequently, this paper proposes Light-ViTeYOLO, a lightweight pine wilt disease detection method based on Vision Transformer-enhanced YOLO (You Only Look Once). A novel lightweight multi-scale attention module is introduced to construct an EfficientViT feature extraction network for global receptive field and multi-scale learning. A novel neck network, CACSNet(Content-Aware Cross-Scale bidirectional fusion neck network), is designed to enhance the detection of diseased trees at single granularity, and the loss function is optimized to improve localization accuracy. The algorithm effectively reduces the number of parameters and giga floating-point operations per second (GFLOPs) of the detection model while enhancing overall detection performance. Experimental results demonstrate that compared with other baseline algorithms, Light-ViTeYOLO proposed in this paper has the least parameter and computational complexity among related algorithms, with 3.89 MFLOPs and 7.4 GFLOPs, respectively. The FPS rate is 57.9 (frames/s), which is better than the original YOLOv5. Meanwhile, its mAP@0.5:0.95 is the best among the baseline algorithms, and the recall and mAP@0.5 slightly decrease. Our Light-ViTeYOLO is the first lightweight method specifically designed for detecting pine wilt disease. It not only meets the requirements for real-time detection of pine wilt disease outbreaks but also provides strong technical support for automated forestry work.

Aerial Image Segmentation of Nematode-Affected Pine Trees with U-Net Convolutional Neural Network

Pine wood nematode disease, commonly referred to as pine wilt, poses a grave threat to forest health, leading to profound ecological and economic impacts. Originating from the pine wood nematode, this disease not only causes the demise of pine trees but also casts a long shadow over the entire forest ecosystem. The accurate identification of infected trees stands as a pivotal initial step in developing effective prevention and control measures for pine wilt. Nevertheless, existing identification methods face challenges in precisely determining the disease status of individual pine trees, impeding early detection and efficient intervention. In this study, we leverage the capabilities of unmanned aerial vehicle (UAV) remote sensing technology and integrate the VGG classical small convolutional kernel network with U-Net to detect diseased pine trees. This cutting-edge approach captures the spatial and characteristic intricacies of infected trees, converting them into high-dimensional features through multiple convolutions within the VGG network. This method significantly reduces the parameter count while enhancing the sensing range. The results obtained from our validation set are remarkably promising, achieving a Mean Intersection over Union (MIoU) of 81.62%, a Mean Pixel Accuracy (MPA) of 85.13%, an Accuracy of 99.13%, and an F1 Score of 88.50%. These figures surpass those obtained using other methods such as ResNet50 and DeepLab v3+. The methodology presented in this research facilitates rapid and accurate monitoring of pine trees infected with nematodes, offering invaluable technical assistance in the prevention and management of pine wilt disease.