Citrus Leaf Research Articles

Enhancing agrochemical delivery in citrus leaves with geraniol: a promising strategy for controlling huanglongbing (HLB).

Citrus huanglongbing (HLB) is a devastating disease in citrus, caused by Candidatus Liberibacter asiaticus (CLas), which primarily resides in the phloem where chemicals cannot effectively reach, posing a significant challenge in controlling HLB. To address these challenges, plant essential oils (EOs), widely used as transdermal enhancers and known for their benefits for plant tissues, were investigated for their potential to enhance chemical permeation. In this study, seven EOs - eugenol, carvacrol, eucalyptol, geraniol, linalool, cinnamaldehyde, and d-limonene - were evaluated for their potential to enhance chemical penetration into citrus leaves. Preliminary screening with tracer methods revealed that geraniol provided the greatest enhancement of permeation, achieving the deepest and broadest distribution into the spongy tissue of the citrus leaf. The permeation levels of rhodamine B and crystal violet through the cuticular layer were elevated from 0.55 mg L-1 and 0.11 mg L-1 to 20.04 mg L-1 and 7.14 mg L-1, respectively, by the addition of geraniol. Further validation through high-performance liquid chromatography-mass spectrometry (HPLC-MS) confirmed that the penetration of fludioxonil and tetracycline into citrus leaf tissues was enhanced by 15.07-fold and 20.43-fold, respectively. In planta experiments confirmed that adding geraniol to tetracycline and oxytetracycline via foliar application eliminated CLas bacteria in HLB-affected citrus more efficiently than controls. Our study underscores the potential of geraniol as an effective EO-based permeation enhancer for combating HLB. By significantly improving the delivery and efficacy of bactericidal treatments, geraniol offers a promising strategy for managing this catastrophic citrus disease, potentially transforming the approach to controlling HLB. © 2024 Society of Chemical Industry.

Pesticide Residue Coverage Estimation on Citrus Leaf Using Image Analysis Assisted by Machine Learning

Globally, the agricultural industry has benefited from using pesticides to minimize crop losses. Nevertheless, the indiscriminate overuse of pesticides has led to significant risks associated with a detrimental impact on the environment and human health. Therefore, emerging concerns of pesticide residue found in crops, food, and livestock are a pressing issue. To address the above challenges, there have been many efforts made towards implementing machine learning to enable precision agricultural practices to reduce pesticide overuse. As of today, there are no guiding digital tools available for citrus growers to provide pesticide residue leaf coverage analysis after foliar applications. Herein, we are the first to report software assisted by lightweight machine learning (ML) to determine the Kocide 3000 and Oxytetracycline (OTC) residue coverage on citrus leaves based on image data analysis. This tool integrates a foundational Segment Anything Model (SAM) for image preprocessing to isolate the area of interest. In addition, Kocide 3000 and Oxytetracycline (OTC) residue coverage analysis was carried out using a specialized Mask Region-Based Convolutional Neural Network (CNN). This CNN was pre-trained on the MS COCO dataset and fine-tuned by training with acquired datasets in laboratory and field conditions. The developed software demonstrated excellent performance on both pesticides’ accuracy, precision, and recall, and F1 score metrics. In summary, this tool has the potential to assist growers with the decision-making process for controlling pesticide use rate and frequency, minimizing pesticide overuse.

Impedimetric immunosensor versus qPCR for Huanglongbing detection

Citriculture is facing challenges due to the spread of various diseases, among which the most threatening, with worldwide occurrence, is Huanglongbing (HLB), caused by the bacteria Candidatus Liberibacter spp., vectored by psyllids. In orchards, HLB-infected plants are identified by visual observation of symptoms. For laboratory diagnosis, the gold standard is the polymerase chain reaction (PCR), requiring expertise and specific equipment, and with high financial cost. In this work, a selective impedimetric immunosensor was developed for the detection and determination of Ca. L. asiaticus (CLas) in citrus samples. An anti-HLB antibody against the outer membrane protein (OMP) sequence of CLas was obtained and immobilized on previously synthesized and characterized magnetic nanoparticles. The immobilized antibody was presented to various citrus leaf sample extracts. After the affinity reaction with the antigen, a washing step was performed to minimize matrix effects. The affinity reaction was monitored by electrochemical impedance spectroscopy, using a glassy carbon working electrode containing a neodymium magnet. The developed device was able to distinguish HLB-positive samples from HLB-negative samples and those with other infections. The results obtained with the proposed methodology were in good agreement with quantitative PCR (qPCR).

The starch excess and key genes underlying citrus leaf chlorosis by rootstock-scion incompatibility

Leaf chlorosis caused by rootstock-scion incompatibility in citrus orchard badly affects fruit yield and quality. Starch excess and its key genes underlying citrus leaf chlorosis in incompatible graft remained unknown. Here, using created model incompatible/ compatible rootstock-scion combinations, we investigated starch content and distribution in 116 various chlorotic leaves of incompatible graft, and characterized the relationship between leaf chlorosis and starch accumulation. Further, we identified starch metabolism-related gene families by genome-wide analysis of pomelo genome, and performed comparative transcriptomic analysis on leaves. A total of nine key differentially expressed genes of starch metabolism were validated. Among them, seven starch synthesis-related genes were significantly upregulated, and two starch degradation-related genes, CgBAM4 and CgBAM6, were significantly downregulated. Meanwhile, the relative expression of synthesis-related genes was positively correlated with starch accumulation and leaf chlorosis. Using transient overexpression and VIGS experiments in pomelo, we confirmed the function of CgGBSS2, which was the only amylose synthesis-related key gene with the most significantly upregulated expression level. We proposed a working model to illustrate the regulatory network of starch excess accumulation involving in citrus leaf chlorosis of incompatible graft in the end. This study provides insights into the molecular mechanism underlying leaf chlorosis process in rootstock-scion incompatibility.

Classification of Citrus Leaf Diseases Using Hyperspectral Reflectance and Fluorescence Imaging and Machine Learning Techniques

Citrus diseases are significant threats to citrus groves, causing financial losses through reduced fruit size, blemishes, premature fruit drop, and tree death. The detection of citrus diseases via leaf inspection can improve grove management and mitigation efforts. This study explores the potential of a portable reflectance and fluorescence hyperspectral imaging (HSI) system for detecting and classifying a control group and citrus leaf diseases, including canker, Huanglongbing (HLB), greasy spot, melanose, scab, and zinc deficiency. The HSI system was used to simultaneously collect reflectance and fluorescence images from the front and back sides of the leaves. Nine machine learning classifiers were trained using full spectra and spectral bands selected through principal component analysis (PCA) from the HSI with pixel-based and leaf-based spectra. A support vector machine (SVM) classifier achieved the highest overall classification accuracy of 90.7% when employing the full spectra of combined reflectance and fluorescence data and pixel-based analysis from the back side of the leaves, whereas a discriminant analysis classifier yielded the best accuracy of 94.5% with the full spectra of combined reflectance and fluorescence data and leaf-based analysis. Among the diseases, control, scab, and melanose were classified most accurately, each with over 90% accuracy. Therefore, the integration of the reflectance and fluorescence HSI with advanced machine learning techniques demonstrated the capability to accurately detect and classify these citrus leaf diseases with high precision.

Citrus yellow vein clearing virus infection triggers phloem remobilization of iron- and zinc-nicotianamine in citrus.

Citrus yellow vein clearing virus (CYVCV) is a worldwide and highly destructive disease of citrus, but the mechanisms involved in CYVCV-inhibited plant growth are not well understood. This study examined nutrient levels and their cellular distribution in different organs of healthy and CYVCV-affected citrus (Citrus reticulata 'Kanpei') plants. We found that CYVCV-infected plants exhibit characteristic symptoms, including a significant reduction in iron (Fe) and other elemental nutrients in the shoots. Our data suggest that CYVCV-induced chlorosis in citrus leaf veins is primarily due to iron deficiency, leading to reduced chlorophyll synthesis. Further analysis revealed a marked decrease in iron concentration within the pith and xylem of citrus petioles post-CYVCV infection, contrasting with increased Fe and zinc (Zn) concentrations in the phloem. Moreover, a substantial accumulation of starch granules was observed in the pith, xylem, and phloem vessels of infected plants, with vessel blockage due to starch accumulation reaching up to 81%, thus significantly obstructing Fe transport in the xylem. Additionally, our study detected an upregulation of genes associated with nicotinamide metabolism and Fe and Zn transport following CYVCV infection, leading to increased levels of nicotinamide metabolites. This suggests that CYVCV-infected citrus plants may induce nicotinamide synthesis in response to Fe deficiency stress, facilitating the transport of Fe and Zn in the phloem as nicotinamide-bound complexes. Overall, our findings provide insight into the mechanisms of long-distance Fe and Zn transport in citrus plants in response to CYVCV infection and highlight the role of nutritional management in mitigating the adverse effects of CYVCV, offering potential strategies for cultivating CYVCV-resistant citrus varieties.

First Report of Citrus Leaf Blotch Virus on Nagami Kumquat in Turkey.

Kumquat, known as the smallest of citrus fruits, has seen an increase in the number of commercial orchards in recent years. In May of 2022, a PCR-based survey for citrus virus diseases was conducted in commercial Nagami kumquat (Fortunella margarita (Lour.) Swingle) orchards in the Cukurova region of Turkey. During the survey, five trees with bud union disorder symptoms were found in two different kumquat orchards in the Erdemli district of Mersin province. Young leaf samples from the five trees showing bud union disorder symptoms and two symptomless trees were collected from 6-year-old kumquat. Common citrus viruses and viroids, such as citrus psorosis virus, citrus exocortis viroid, and hop stunt viroid, were not detected in PCR tests performed on all collected samples. The samples were also tested for citrus leaf blotch virus (CLBV; genus Citrivirus, family Betaflexiviridae), a virus associated with a bud union crease in kumquat (Vives et al.2001). Total RNA was isolated from collected leaf samples using the RNeasy Plant Mini Kit (Qiagen, USA). In the RT-PCR assay, two separate specific primer pairs were used to determine the presence of CLBV. The coat protein gene (CP), was amplified with primers KU-18 (5'-TTAAGATTACAGACACGAAGG- 3') and KU-19 (5' CTGTTTTTGAATTTTGCTCG-3'), and the RNA-dependent RNA polymerase gene (RdRp) was amplified with KU-27(5'-GATGCAAGCCAGGATGAATAC-3') and KU-15 (5'-CAGACACTCCAAGACCTTTCC-3') primers (Vives et al. 2002). All of symptomatic samples yielded amplicons of the expected size (456-bp RdRp and 438-bp CP). No amplicons were obtained from symptomless samples. Two PCR products were sequenced to confirm their identity by direct sequencing (GenBank accession no: OQ718432, OQ718433, PP726107, PP726108). Consensus sequences of these two genes showed 96% and 97% nt identity with CP and RdRP genes of the CLBV sequence in GenBank (MT038390, EU857539), respectively. For biological indexing, four RT-PCR-positive samples were used as inoculum sources. Four of Etrog citron (C. medica) and four of Dweet tangor (C. tangerina) seedlings were graft-inoculated for each source, and two seedlings for each cultivar were used as a negative control. Two months later, chlorotic blotching symptoms were observed on Dweet tangor leaves. Six months later, Etrog citron exhibited stem pitting symptoms. None of the negative control seedlings developed any symptoms. RT-PCR was performed to confirm the presence of CLBV in the virus-inoculated seedlings, and amplicons of the expected sizes (456-bp RdRp and 438-bp CP) were obtained. To the best of our knowledge, this is the first report of the CLBV infection in citrus in Turkey. CLBV is thought to have originated from infected grafting material in the country, although it is not known where it may have originated. Further research is needed to determine the distribution of CLBV and its potential to impact commercial citrus production in Turkey.

Combining Transfer Learning and Ensemble Algorithms for Improved Citrus Leaf Disease Classification

Accurate categorization and timely control of leaf diseases are crucial for citrus growth. We proposed the Multi-Models Fusion Network (MMFN) for citrus leaf diseases detection based on model fusion and transfer learning. Compared to traditional methods, the algorithm (integrating transfer learning Alexnet, VGG, and Resnet) we proposed can address the issues of limited categories, slow processing speed, and low recognition accuracy. By constructing efficient deep learning models and training and optimizing them with a large dataset of citrus leaf images, we ensured the broad applicability and accuracy of citrus leaf disease detection, achieving high-precision classification. Herein, various deep learning algorithms, including original Alexnet, VGG, Resnet, and transfer learning versions Resnet34 (Pre_Resnet34) and Resnet50 (Pre_Resnet50) were also discussed and compared. The results demonstrated that the MMFN model achieved an average accuracy of 99.72% in distinguishing between diseased and healthy leaves. Additionally, the model attained an average accuracy of 98.68% in the classification of multiple diseases (citrus huanglongbing (HLB), greasy spot disease and citrus canker), insect pests (citrus leaf miner), and deficiency disease (zinc deficiency). These findings conclusively illustrate that deep learning model fusion networks combining transfer learning and integration algorithms can automatically extract image features, enhance the automation and accuracy of disease recognition, demonstrate the significant potential and application value in citrus leaf disease classification, and potentially drive the development of smart agriculture.

Profiling of the Citrus Leaf Endophytic Mycobiota Reveals Abundant Pathogen-Related Fungal Groups.

Plant endophytic microbial communities consist of many latent plant pathogens and, also, many pathogen-related species with reduced virulence. Though with a long history of co-evolution, the diversity and composition of the endophytic mycobiota, especially the pathogen-related fungal groups, has been under-investigated in Citrus (C.). Based on the amplicon sequencing of fungal internal transcribed spacer (ITS), the leaf endophytic mycobiota were profiled on citrus varieties from different citrus-producing regions. The pomelo variety shared significantly distinctive leaf mycobiota when compared to the mandarin and sweet orange; these conform to their host genetic relationships. In addition, a data set of 241 citrus-related fungi, including 171 (71%) pathogens and potential pathogens, was summarized from previous studies. Under the criteria of local BLAST (covered ITS nucleotide ≥ 150 bp, sequence identity ≥ 99%), a total of 935 fungal operational taxonomic units (OTUs) were assigned to 62 pathogen-related fungal groups, representing 14.9% of the relative abundance in the whole community. Of which, the top groups consisted of Colletotrichum gloeosporioides (mean relative abundance, 4.3%), Co. citricola and Co. karstii (2.7%), Zasmidium citri-griseum (2.4%), and Z. fructigenum (1.4%). At the genus level, the ratio of the pathogen-related fungal groups in 64% of fungal genera (16 out of 25) exceeded 50%, which are the solely or mainly occurring fungi of their genus in citrus. Our study suggests that the leaf endophytic compartment may be an important place for the growth of latent pathogens.

Citrus leaf disease detection through deep learning approach

The majority of people in the world directly or indirectly depend on agriculture. Plant diseases are a significant threat to agricultural production and food security. Due to its high nutritional value, citrus fruit is one of the most abundant fruits in the world. However, different diseases are responsible for degraded citrus production as well as financial losses to the farmers. Traditionally, visual observation by experts has been attended to diagnose plant diseases. Usually, plant leaf disease recognition methods mainly rely on expert experiences to manually extract the colour, composition, and other features of diseased leaf images. Black spot, greening, canker, and melanoses are four common citrus leaf diseases. Rapid and accurate diagnosis of these diseases is a demand of time. Deep learning is a promising solution to these problems. There are different types of deep learning architecture like ImageNet, GoogleNet, VGG16, ResNet50, and InceptionV3, which show promising results in different object detection. Though most of these benchmark models give almost similar accuracy. However, this paper uses two deep learning models to find the better ones for the detection of citrus leaf disease detection. Hence, InceptionV3 outperforms VGG16 in terms of accuracy.

Lightweight citrus leaf disease detection model based on ARMS and cross-domain dynamic attention

In citrus cultivation, Anthracnose, Scab, and Greasy Spot significantly impact yield and quality. Facing challenges in detecting small targets against complex orchard backgrounds with uneven lighting and obstructions, existing models suffer from low detection accuracy. This study introduces the YOLOv8n-CDDA citrus leaf disease detection model. The Cross-Domain Dynamic Attention (CDDA) mechanism deconstructs the backbone network’s input feature maps into sections, dynamically assigning spatial and channel attention weights to reconstruct critical information and capture the variations and weak semantic features of disease textures. The proposed Adaptive Random Mix-Cut Splicing (ARMS) image augmentation technique blends diseased leaf images with healthy citrus backgrounds, enhancing the diversity and number of background targets. To reduce computational and memory consumption, the network is streamlined through channel pruning; to compensate for the loss in accuracy from pruning, a teacher–assistant–student network format is used for knowledge distillation, where the student network learns from soft knowledge to improve disease recognition accuracy. Finally, Grad-CAM++ technology generates heatmaps of the detections, facilitating the visualization of effective features and deepening understanding of the model’s focus areas. Experimental results demonstrate that the YOLOv8n-CDDA model achieves an average accuracy of 90.89% in disease detection, with an average recall rate of 81.12%, and a mean Average Precision (mAP50) of 88.36%. Compared to the original YOLO v8n and current mainstream detection models such as YOLOv5s, SSD, and Faster-RCNN, the improvements in average accuracy are respectively 2.95%, 4.78%, 14.22%, and 21.01%; in average recall, 2.36%, 3.09%, 15.74%, and 23.27%; and in mAP50, 2.38%, 3.13%, 13.45%, and 20.91%. After pruning and distillation for lightweight adaptation, the YOLOv8n-CDDA model has a parameter size of 0.8M, requires 4.2 GFLOPs, weighs 2.0 MB, and operates at 45 fps. Compared to YOLOv8n, this represents a reduction of 2.2M in parameters, 3.9 GFLOPs, and 4 MB in model weight, with an increase of 7 fps in speed. This model exhibits exceptional performance in the complex environment of citrus leaf disease detection, providing robust technical support for citrus growth monitoring studies, and offering insights for disease detection in other crops as well.

Chemical Variability, Antioxidant and Larvicidal Efficacy of EOs from Citrus sinensis (L.) Osbeck Peel, Leaf, and Flower

Essential oils (EOs) from Citrus sinensis (Rutaceae) possess diverse biological activities. However, a comprehensive comparison of their chemical composition and bioactivity across different plant parts has not been studied yet. The current research comparatively assesses the yield, chemical composition, chiral distribution, antioxidant properties, and larvicidal activity of EOs extracted from the peels, leaves, and flowers of C. sinensis. EOs extracted via hydro-distillation (HD) and steam distillation (SD) were analyzed by gas chromatography–mass spectrometry (GC-MS) and chiral GC-MS to explore their chemical composition and enantiomeric distribution. In addition, their larvicidal and antioxidant potentials were evaluated following standard protocols. Peels of C. sinensis exhibited significantly higher oil content (1.75–2.25%) compared to its leaves (0.75–0.78%) and flowers (0.20–0.25%). The GC-MS analysis identified around 60 compounds, including terpenoids, sesquiterpenoids, and oxygenated terpenoids in the HD and SD extractions. Higher concentrations of sabinene were found in flower extract (38.05–39.89%) and leaf extract (32.30–36.91%), while peel extract contained more than 90% limonene. The larvicidal activity of peel oil was primarily attributed to limonene, with an LC50 value of 0.0031 µL/mL. The current study reports the first chiral (GC-MS) analysis in the essential oil of the leaves and flowers of C. sinensis, paving the way for authenticity and purity. Furthermore, the chemical profiling of citrus EOs, particularly from the peel, demonstrates a safe and promising candidate for diverse biological applications.

Bio-efficacy of bio-rationals against citrus leaf miner (Phyllocnistis citrella) in acid lime (Citrus aurantiifolia)

The field experiment was conducted during winter (rabi) 2020–21 and rainy (kharif) season of 2021–22 at College of Agriculture (University of Agricultural Sciences, Dharwad), Vijayapura, Karnataka to evaluate efficacy of bio-rationals against citrus leaf miner (Phyllocnistis citrella Stainton). The experiment was conducted in a randomized block design (RBD) with 8 treatments, viz. bio digester solution (10%); pongamia leaf extract (5%); Prosopis juliflora leaf extract (5%); Beauveria bassiana (2 × 108 conidia/g) (1 kg/ha); Bacillus thuringiensis 8 L (1 L/ha); neem-based insecticide (10000 ppm) (1.5 L/ha); thiamethoxam 25 wg (125 ml/ha) (standard check); and untreated control (water spray), replicated thrice. Among the selected bio-rationals, two application of neem (Azadirachta indica)-based insecticide recorded significantly lesser number of live mines/shoot followed by bio-digester solution (10%) and these treatments were found superior in controlling citrus leaf miner on acid lime [Citrus aurantiifolia (Christm.) Swingle]. The pongamia leaf extract, Bacillus thuringiensis 8 L and Beauveria bassiana were proved to be moderately affective against this pest. The bio-rationals found effective can be used in integrated management of citrus leaf miner on acid lime.

Development of TaqMan RT-qPCR for the detection of regulated citrus viruses and viroids in Aotearoa New Zealand

The major citrus species include several economically important fruits, such as orange, mandarin, lemon, limes, grapefruit and pomelos. Since the 1980 s, total production and consumption of citrus has grown strongly with the current annual worldwide production at over 105 million tonnes. New Zealand's citrus exports, for instance, had an estimated worth of NZ$ 11.6 million (approx. US$ 7 million) in 2020. Citrus plants are prone to viral diseases, which can lead to substantial economic losses. In New Zealand, the citrus Import Health Standard (IHS) has identified 22 viruses and viroids that are subject to regulation and requires citrus nursery stock to be free of these pathogens. As such, there is a need for reliable, sensitive, and rapid detection methods to screen for these viruses and viroids during post entry quarantine. In this study, we developed TaqMan RT-qPCR assays for the detection of nine of these regulated viruses and viroids, namely citrus leaf rugose virus (CiLRV), citrus leprosis virus C (CiLV-C), citrus leprosis virus C2 (CiLV-C2), citrus leprosis virus N (CiLV-N), citrus psorosis virus (CPsV), citrus yellow mosaic virus (CYMV), citrus bent leaf viroid (CBLVd), citrus viroid V (CVd-V), and citrus viroid VI (CVd-VI). These assays have been validated and found to be highly sensitive, specific, and reliable. The implementation of these assays will facilitate the safe importation of citrus nursery stock, thus safeguarding the country's horticultural and economic interests.

E-Pest Surveillance for Assessment of Seasonal Occurrence of Orange Pests in Maharashtra

Monitoring of incidence and intensity of pests over the crop season is crucial for effective pest management. ICT (Information and communication technology) not only enables the assimilation of pest databases over space and time, but also aids in the rapid processing of data to make pest management decisions using the available knowledge base and essential inputs that can be mobilised and adopted for plant protection on wide area basis. This study generated valuable data on the population dynamics of orange pests over the seasonal months from 2012 to 2022 with the aid of applied information and communication technology tools. The majority of the pests under current study showed their seasonal activity throughout the monitoring period. The study on the seasonal incidence revealed bark eating caterpillar Inderbela sp. having their peak infestation during September and October whereas January to March were observed as prime months for fruit sucking moths Othreis sps to reach their peak prevalence and damage. The citrus leaf miner Phyllocnistis citrella population dynamics showed two peaks in August and September. The data generated in this study would help in the forecast of the pests and helpful in devising an effective IPM strategy.

Plant-Derived, Nodule-Specific Cysteine-Rich Peptides as a Novel Source of Biopesticides for Controlling Citrus Greening Disease.

Nodule-specific cysteine-rich (NCR) peptides, encoded in the genome of the Mediterranean legume Medicago truncatula (barrelclover), are known to regulate plant-microbe interactions. A subset of computationally derived 20-mer peptide fragments from 182 NCR peptides was synthesized to identify those with activity against the unculturable vascular pathogen associated with citrus greening disease, 'Candidatus Liberibacter asiaticus' (CLas). Grounded in a design of experiments framework, we evaluated the peptides in a screening pipeline involving three distinct assays: a bacterial culture assay with Liberibacter crescens, a CLas-infected excised citrus leaf assay, and an assay to evaluate effects on bacterial acquisition by the nymphal stage of hemipteran vector Diaphorina citri. A subset of the 20-mer NCR peptide fragments inhibits both CLas growth in citrus leaves and CLas acquisition by D. citri. Two peptides induced higher levels of D. citri mortality. These findings reveal 20-mer NCR peptides as a new class of plant-derived biopesticide molecules to control citrus greening disease.

Identification and Distribution of the 'Candidatus Liberibacter asiaticus'-Asian Citrus Psyllid Pathosystem in Saudi Arabia.

Citrus greening disease was first reported in Saudi Arabia during the 1970s when characteristic foliar and fruit symptoms were observed in commercial citrus groves. However, 'Candidatus Liberibacter asiaticus' (CLas) was not detected in symptomatic trees until 1981 to 1984 when CLas-like cells were observed by transmission electron microscopy in leaves collected from symptomatic citrus groves in southwestern Saudi Arabia. Despite the anticipated establishment of the CLas-Asian citrus psyllid (ACP) (Diaphorina citri Kuwayama) pathosystem, CLas presence has not been verified in suspect trees nor have ACP infestations been documented. Given the recent expansion of citrus production in Saudi Arabia, a systematic country-wide survey was carried out to determine the potential CLas distribution in the 13 citrus-growing regions of the country. Citrus trees were surveyed for the presence of CLas-psyllid vector(s) and characteristic disease symptoms in commercial and urban citrus trees. Adult psyllids collected from infested citrus trees were identified as ACP based on morphological characteristics. Real-time quantitative PCR amplification (qPCR) of the CLas β-subunit of the ribonucleotide reductase (RNR) gene from citrus leaf and fruit samples and/or ACP adults revealed that trees were positive for CLas detection in 10 of the 13 survey regions; however, CLas was undetectable in ACP adults. Phylogenetic and single nucleotide polymorphism (SNP) analyses of a PCR-amplified, cloned fragment of the CLas 16S rRNA gene (∼1.1 kbp) indicated Saudi Arabian isolates were most closely related to Florida, U.S.A., isolates. Analysis of climate variables indicated that the distribution of the ACP-CLas pathosystem observed in Saudi Arabia was consistent with published predictions of terrains most likely to support establishment.

Viral Metatranscriptomic Analysis to Reveal the Diversity of Viruses Infecting Satsuma Mandarin (Citrus unshiu) in Korea.

Citrus cultivation plays a pivotal role, making a significant contribution to global fruit production and dietary consumption. Accurate identification of viral pathogens is imperative for the effective management of plant viral disease in citrus crops. High-throughput sequencing serves as an alternative approach, enabling comprehensive pathogen identification on a large scale without requiring pre-existing information. In this study, we employed HTS to investigate viral pathogens infecting citrus in three different regions of South Korea: Jejudo (Jeju), Wando-gun (Wando), and Dangjin-si (Dangjin). The results unveiled diverse viruses and viroids that exhibited regional variations. Notably, alongside the identification of well-known citrus viruses such as satsuma dwarf virus, citrus tatter leaf virus, and citrus leaf blotch virus (CLBV), this study also uncovered several viruses and viroids previously unreported in Korean citrus. Phylogenetic analysis revealed that majority of identified viruses exhibited the closest affilations with isolates from China or Japan. However, CLBV and citrus viroid-I-LSS displayed diverse phylogenetic positions, reflecting their regional origins. This study advances our understanding of citrus virome diversity and regional dynamics through HTS, emphasizing its potential in unraveling intricate viral pathogens in agriculture. Consequently, it significantly contributes to disease management strategies, ensuring the resilience of the citrus industry.

Occurrence and Multiplex PCR Detection of Citrus Yellow Vein Clearing Virus in Korea.

Citrus yellow vein clearing virus (CYVCV) is a member of the Alphaflexiviridae family that causes yellow vein clearing symptoms on citrus leaves. A total of 118 leaf samples from nine regions of six provinces in Korea were collected from various citrus species in 2020 and 2021. Viral diagnosis using next-generation sequencing and reverse transcription polymerase chain reaction (RT-PCR) identified four viruses: citrus tristeza virus, citrus leaf blotch virus, citrus vein enation virus, and CYVCV. A CYVCV incidence of 9.3% was observed in six host plants, including calamansi, kumquat, Persian lime, and Eureka lemon. Among the citrus infected by CYVCV, only three samples showed a single infection; the other showed a mixed infection with other viruses. Eureka lemon and Persian lime exhibited yellow vein clearing, leaf distortion, and water-soak symptom underside of the leaves, while the other hosts showed only yellowing symptoms on the leaves. The complete genome sequences were obtained from five CYVCV isolates. Comparison of the isolates reported from the different geographical regions and hosts revealed the high sequence identity (95.2% to 98.8%). Phylogenetic analysis indicated that all the five isolates from Korea were clustered into same clade but were not distinctly apart from isolates from China, Pakistan, India, and Türkiye. To develop an efficient diagnosis system for the four viruses, a simultaneous detection method was constructed using multiplex RT-PCR. Sensitivity evaluation, simplex RT-PCR, and stability testing were conducted to verify the multiplex RT-PCR system developed in this study. This information will be useful for developing effective disease management strategies for citrus growers in Korea.

Reaction of Selected Citrus Cultivars to Pseudocercospora Leaf and Fruit Spot Disease Under Natural Infection in Northern Uganda

Citrus is an important crop among many resource constrained subsistence farmers living in rural areas of Uganda. Citrus production is affected mainly by drought, declining soil fertility, pests and diseases. Among diseases, citrus leaf and fruit spot disease caused by fungus Pseudocercospora angolensis is currently one of the major constraints to the production of citrus in Uganda where millions of people rely on the crop for nutritional security and household income. The disease can lead to 50-100% fruit yield loss depending on environmental conditions, disease management and weather conditions. In order to identify resistance to P. angolensis, a study was conducted to identify variability for citrus leaf and fruit spot resistance from adapted commercial cultivars as an initial step in developing integrated disease management strategy. Six cultivars were assessed. The screening was under natural infection conditions in disease hot spots in northern Uganda in 2014a and 2014b seasons. The results showed significance difference (p < 0.01) for Area Under Disease Progressive Curve (AUDPC) for number of leaves with P. angolesnis symptoms and number of lesions. Subsequently, the study identified Kuno as resistant and Tangelo as less susceptible to Pseudocercospora leaf and fruit spot infection, and they could be recommended for citrus leaf and fruit spot disease control.