Citrus Disease Research Articles

Genetic Diversity of "Candidatus Liberibacter asiaticus" Based on Four Hypervariable Genomic Regions in China.

Huanglongbing (HLB; greening disease), caused by Candidatus Liberibacter asiaticus (CLas), is the most damaging citrus disease worldwide. The disease has spread throughout the citrus-producing regions of Guangxi, Guangdong, Fujian, and others in China. A total of 1,788 HLB-like symptomatic or asymptomatic samples were collected from the Guangxi and Fujian provinces of China to decipher the genetic diversity of CLas and its correlation with geographic region and host plant. The disease was the most severe in orange and the least in pomelo. CLas bacteria associated with the specific geographical and citrus variety infected more than 50% of the HLB-like symptomatic samples. We identified 6,286 minor variations by comparing 35 published CLas genomes and observed a highly heterogeneous variation distribution across the genome, including four highly diverse nonprophages and three prophage segments. Four hypervariable genomic regions (HGRs) were identified to determine the genetic diversity among the CLas isolates collected from Guangxi and Fujian, China. A phylogenetic tree constructed from four HGRs showed that 100 CLas strains could be separated into four distinct clades. Ten new strains with high variations of prophage regions were identified in the mandarin and tangerine grown in new plantation areas of Guangxi. Characterizing these HGR variations in the CLas bacteria may provide insight into their evolution and adaptation to host plants and insects. IMPORTANCE The hypervariable genomic regions derived from 35 published CLas genomes were used to decipher the genetic diversity of CLas strains and identify 10 new strains with high variations in prophage regions. Characterizing these variations in the CLas bacteria might provide insight into their evolution and adaptation to host plants and insects in China.

Automatic Classification Service System for Citrus Pest Recognition Based on Deep Learning.

Plant diseases are a major cause of reduction in agricultural output, which leads to severe economic losses and unstable food supply. The citrus plant is an economically important fruit crop grown and produced worldwide. However, citrus plants are easily affected by various factors, such as climate change, pests, and diseases, resulting in reduced yield and quality. Advances in computer vision in recent years have been widely used for plant disease detection and classification, providing opportunities for early disease detection, and resulting in improvements in agriculture. Particularly, the early and accurate detection of citrus diseases, which are vulnerable to pests, is very important to prevent the spread of pests and reduce crop damage. Research on citrus pest disease is ongoing, but it is difficult to apply research results to cultivation owing to a lack of datasets for research and limited types of pests. In this study, we built a dataset by self-collecting a total of 20,000 citrus pest images, including fruits and leaves, from actual cultivation sites. The constructed dataset was trained, verified, and tested using a model that had undergone five transfer learning steps. All models used in the experiment had an average accuracy of 97% or more and an average f1 score of 96% or more. We built a web application server using the EfficientNet-b0 model, which exhibited the best performance among the five learning models. The built web application tested citrus pest disease using image samples collected from websites other than the self-collected image samples and prepared data, and both samples correctly classified the disease. The citrus pest automatic diagnosis web system using the model proposed in this study plays a useful auxiliary role in recognizing and classifying citrus diseases. This can, in turn, help improve the overall quality of citrus fruits.

Citrus Huanglongbing correlated with incidence of Diaphorina citri carrying Candidatus Liberibacter asiaticus and citrus phyllosphere microbiome.

In China, citrus Huanglongbing (HLB) disease is caused by the Candidatus Liberibacter asiaticus bacterium, which is carried by the Asian citrus psyllid Diaphorina citri Kuwayama. It was hypothesized that the epidemic of the HLB may related with the rate of bacterium presence in the insect vector and bacterium content in plant tissues, as well as the phyllosphere microbe communities changes. This study systematically analyzed the presence or absence of Ca. L. asiaticus in citrus tree leaves and in the insect vector D. citri over a 6-year period using real-time PCR. In addition, changes in the number of bacteria carried by D. citri over 12 months were quantified, as well as the relationship between the proportion of D. citri carrying Ca. L. asiaticus and the proportion of plants infected with Ca. L. asiaticus were analyzed. Results showed that the proportion of D. citri carrying bacteria was stable and relatively low from January to September. The bacteria in citrus leaves relatively low in spring and summer, then peaked in December. The proportion of D. citri carrying bacteria gradually declined from 2014 to 2019. The proportion of D. citri carrying Ca. L. asiaticus showed a significant positive correlation with the proportion of diseased citrus. The phyllosphere bacterial and fungal communities on the healthy citrus leaf were significantly different with the disease leaf in April and December. Pathogenic invasions change the citrus phyllosphere microbial community structure. It could be summarized that citrus Huanglongbing correlated with incidence of Diaphorina citri carrying Candidatus Liberibacter asiaticus and citrus phyllosphere microbiome.

An Improved Recombinase Polymerase Amplification Coupled with Lateral Flow Assay for Rapid Field Detection of 'Candidatus Liberibacter asiaticus'.

Huanglongbing (HLB) is a destructive citrus disease that affects citrus production worldwide. 'Candidatus Liberibacter asiaticus' (CLas), a phloem-limited bacterium, is the associated causal agent of HLB. The current standard for detection of CLas is real-time quantitative polymerase chain reaction (qPCR) using either the CLas 16S rRNA gene or the ribonucleotide reductase (RNR) gene-specific primers/probe. qPCR requires well-equipped laboratories and trained personnel, which is not convenient for rapid field detection of CLas-infected trees. Recombinase polymerase amplification (RPA) assay is a fast, portable alternative to PCR-based diagnostic methods. In this study, an RPA assay was developed to detect CLas in crude citrus extracts utilizing isothermal amplification, without the need for DNA purification. Primers were designed to amplify a region of the CLas RNR gene, and a fluorescent labeled probe allowed for detection of the amplicon in real-time within 8 mins at 39°C. The assay was specific to CLas, and the sensitivity was comparable to qPCR, with a detection limit cycle threshold of 34. Additionally, the RPA assay was combined with a lateral flow device for a point-of-use assay that is field deployable. Both assays were 100% accurate in detecting CLas in fresh citrus crude extracts from leaf midribs and roots from five California strains of CLas tested in the Contained Research Facility in Davis, California. This assay will be important for distinguishing CLas-infected trees in California from those infected by other pathogens that cause similar disease symptoms and can help control HLB spread.

Validation of Propidium Monoazide-qPCR for Assessing Treatment Effectiveness against ‘Candidatus Liberibacter asiaticus’ in Citrus

Huanglongbing (HLB) is an important citrus disease associated with the phloem-limited, uncultured bacterium ‘Candidatus Liberibacter asiaticus’(CLas). Effective treatments against CLas have to be validated in the field, however, methods for the field assessment of treatment effectiveness are time-consuming, in part because DNA-based assays, including quantitative PCR (qPCR), cannot differentiate between live and dead bacterial DNA. The aim of this study was to develop a method for rapid the evaluation of HLB therapies in field experiments. To this aim, a DNA extraction method from citrus leaf tissues with propidum monoazide (PMA), a dye that binds covalently to dsDNA making it unavailable for amplification in subsequent qPCR reactions, was optimized. The results indicated that the efficacy of PMA-qPCR was highly dependent on the primer set used. Primers targeting the 16S region of CLas showed a clear distinction between qPCR from PMA-treated and non-treated samples, while the RNR and LJ900 primers did not show significant differences between the DNA extraction methods. The PMA-qPCR viability analysis of CLas from citrus cuttings treated with different ampicillin (Amp) concentrations showed that all concentrations reduced CLas titers significantly starting 4 days after the initial treatment, unlike the water treatment, which did not show any change. This method was used for assessing the antibacterial activity of Amp, Streptomycin, Oxytetracycline (OTC), and a water control in field tests. The PMA-qPCR results indicated that Amp and OTC displayed significant antibacterial activity against CLas by 8 days post-injection, which was not detected in the non-PMA qPCR analysis. This method could allow the rapid validation of treatments against CLas in field experiments and facilitate the implementation of effective management strategies against HLB.

Citrus Diseases Recognition by Using CNN

Pattern recognition is attracting the interest of researchers in the recently few years as a machine learning approaches due to its vast extending application areas. he application area includes communications, medicine, automations, data mining, military intelligence, document classification, bioinformatics, speech recognition and business. In this research convolutional neural networks (CNN) using for building system to recognize diseases that are happened in citrus. In this study presented dataset for seven classes of citrus diseases which contains 2450 images such as anthracnose, brown rot, citrus black spot, citrus canker, citrus scob, melanose and sooty mold citrus. The proposed system recognizes learned via CNN. The experimental result shows our model has ability to recognize citrus diseases with high and robustness accuracy. The study presented here gives 88% recognition of citrus diseases for the entire database.

Being prepared for huanglongbing disease of citrus: a simulation exercise workshop for contingency planning held in Valencia, Spain

AbstractContingency plans aim to ensure a rapid and effective response to an outbreak of a pest likely which is to have a major impact. Huanglongbing is a devastating disease of citrus not yet present in the European and Mediterranean Plant Protection Organization (EPPO) region. A simulation exercise workshop for contingency planning was held in Valencia, Spain over 3 days in October 2021 as part of the PRE‐HLB project funded by the European Union Horizon 2020 programme. In total, 49 experts from the EPPO region attended the workshop. Participants represented a range of stakeholders from citrus cooperatives, grower associations, nursery workers, citrus exporters, landscapers and gardeners, scientists, Regional Plant Health Authorities, National Plant Protection Organizations and the European Commission. Participants were split into five groups and each group acted as an outbreak management team for a scenario based on a finding of a quarantine pest. The scenario followed the first (fictitious) finding of Diaphorina citri (the insect vector of ‘Candidatus Liberibacter asiaticus’, a causal agent of huanglongbing) at a citrus orchard in Spain. As the scenario developed over a month time scale, each team had to organize themselves to manage a number of issues designed to mimic the development of an outbreak of huanglongbing of citrus over the course of the exercise. The groups acknowledged the usefulness of the Spanish contingency plans for huanglongbing and its vectors, the EPPO Standards, and the risk‐based surveillance design tool RiBESS+.

Integrated Pest Management Strategies for Asian Citrus Psyllid Diaphorina citri Kuwayama (Hemiptera: Psyllidae) and Huanglongbing in Citrus for Sarawak, East Malaysia, Borneo.

Simple SummaryThe severe Asian form of huanglongbing is the most serious disease of citrus in Malaysia. It is caused by a phloem-limited bacterium ‘Candidatus Liberibacter asiaticus’ and transmitted by the Asian citrus psyllid, Diaphorina citri Kuwayama. Minimising the devastating impacts of the disease requires effective integrated pest management (IPM) programs to limit detrimental environmental and health consequences of over-reliance on synthetic pesticides. Key components of such programs include grower awareness, planting of pathogen-free trees, monitoring psyllid and disease incidence, removal of diseased trees, and judicious use of insecticides, mineral oils, and natural enemies to suppress incidence of the psyllid. Significant progress has been made towards implementing the programs.The Asian citrus psyllid (ACP), Diaphorina citri Kuwayama, transmits ‘Candidatus Liberibacter asiaticus’ (CLas), a phloem-limited bacterium associated with the severe Asian form of huanglongbing (HLB), and the most destructive disease of citrus. The pathogen and the psyllid, both of South Asian origin, are now widespread in citrus regions of Asia and the Americas. There is no cure for the disease. Application of synthetic pesticides, in some instances more frequently than fortnightly, to minimise incidence of ACP in citrus orchards, has not prevented inevitable impacts of the disease in regions of Asia where CLas is present. Despite the inevitable spread of the disease, significant progress has been made in Sarawak since the mid-1990s towards effectively implementing integrated pest management (IPM) programs for stemming the impact of the disease and detrimental consequences of over-reliance on synthetic pesticides. Growers are encouraged to plant pathogen-free trees, remove diseased trees, monitor incidence of the psyllid, and to use pesticides judiciously to reduce their detrimental impacts on natural enemies. Knowledge has been enhanced through research on seasonal incidence of the psyllid, use of mineral oils, development of protocols and iodine–starch test kits for detecting infected trees, PCR for confirming the presence of CLas in symptomatic leaves, methods for monitoring incidence the psyllid, and training extension staff and growers. However, major impediments to increasing the average longevity of trees beyond <5 years in poorly managed orchards, based on marcotting (air layering), and >12 years in well-managed orchards, based on pathogen-free trees, still need to be addressed. These include grower knowledge, marcotting, aggressive marketing of synthetic pesticides, high prices of mineral oils, spray application procedures, and better reliance on natural enemies of the psyllid.

Mpp51Aa1 toxicity to Diaphorina citri nymphs demonstrated using a new, long-term bioassay method

While pesticidal proteins from Bacillus thuringiensis have provided for effective management of several insect pests of agricultural importance, few with toxicity to hemipteran species have been identified. The Asian citrus psyllid, Diaphorina citri transmits Candidatus Liberibacter asiaticus (CLas), the presumed bacterial causative agent of the devastating disease citrus greening. Despite the critical role of D. citri nymphs in the acquisition and inoculation of CLas, the lack of a long-term feeding method impedes the screening of Bt proteins for toxicity against nymphs, which play a key role in CLas transmission. Here, we developed a long-term nymph bioassay and determined the toxicity of the Bt pesticidal protein Mpp51Aa1. The new bioassay method allows nymphs to survive for up to six days when maintained on treated folded wipes. The standard hemipteran membrane feeding assay was used to assess Mpp51Aa1 toxicity against D. citri adults. Mpp51Aa1 was toxic to D. citri nymphs with a median lethal concentration (LC50) of 56.5 µg/ml in wipe feeding assays, and to D. citri adults with an LC50 of 110.4 µg/ml in membrane feeding assays. These results demonstrate the utility of this long-term nymph bioassay method and suggest that Mpp51Aa1 has potential for sustainable use in D. citri management toward mitigation of citrus greening disease.

A CRISPR/Cas12a-based photothermal platform for the portable detection of citrus-associated Alternaria genes using a thermometer

The outbreak of citrus brown spot because of Alternaria is one of the most destructive citrus diseases. Additionally, Alternaria species produce highly toxic mycotoxins. Mass screening is a valid method to control the spread of Alternaria. Morphological analysis and polymerase chain reaction combined with gene-sequencing technique are the most commonly used techniques for detecting Alternaria. However, they are limited by either low convenience and accuracy or low instrument accessibility and high cost. To balance the convenience, accuracy, test availability, and low cost, we develop a CRISPR/Cas12a-based photothermal platform for the portable detection of Alternaria genes using a thermometer. Using this platform, the Alternaria genes from the synthetic sequences and cultured fungus of citrus, tomato, and apple can be detected using a thermometer with a detection limit of 1.5 pM. With the aid of the CRISPR/Cas12a system, citrus-associated Alternaria can be specifically differentiated from other citrus disease-associated microorganisms. When the photothermal platform is applied to analyze the citrus fruit samples collected in the field, good-consistency results are obtained with the gene-sequencing technology. The excellent performance of this portable method shows that it can be applied to screen for Alternaria in resource-poor settings.

Automated identification of citrus diseases in orchards using deep learning

Citrus disease identification is vital to ensure the quality and quantity of production and minimise the damage in orchards. This study proposed a deep learning-based algorithm to perform automated identification on five common types of citrus diseases in orchards. The proposed algorithm consisted of a detection network to detect the citrus fruit in the complicated background and a classification network to classify them into the corresponding types. Several state-of-the-art network architectures were studied in terms of their object detection and classification performance, and they were evaluated on a dataset of 1524 images taken in field conditions from different orchards in distinct time intervals, scales, angles, and lighting conditions. Based on the experimental results, the algorithm eventually adopted an optimised YOLO-V4 model for detection and the EfficientNet model for classification, and the overall algorithm obtained the accuracy and F1 score of 0.890 and 0.872, respectively. In conclusion, the proposed algorithm is capable of automated citrus disease identification in orchards featuring high efficiency and precision.

Proteomic and bioinformatic analyses of proteins in the outer membrane and extracellular compartments and outer membrane vesicles of Candidatus Liberibacter species.

Citrus Huanglongbing (HLB) is the most devastating citrus disease in the world. Candidatus Liberibacter asiaticus (Las) is the prevalent HLB pathogen, which is yet to be cultivated. A recent study demonstrates that Las does not contain pathogenicity factors that are directly responsible for HLB symptoms. Instead, Las triggers systemic and chronic immune responses, representing a pathogen-triggered immune disease. Importantly, overproduction of reactive oxygen species (ROS) causes systemic cell death of phloem tissues, thus causing HLB symptoms. Because Las resides in the phloem tissues, it is expected that phloem cell might recognize outer membrane proteins, outer membrane vesicle (OMV) proteins and extracellular proteins of Las to contribute to the immune responses. Because Las has not been cultivated, we used Liberibacter crescens (Lcr) as a surrogate to identify proteins in the OM fraction, OMV proteins and extracellular proteins by liquid chromatography with tandem mass spectrometry (LC–MS/MS). We observed OMVs of Lcr under scanning electron microscope, representing the first experimental evidence that Liberibacter can deliver proteins to the extracellular compartment. In addition, we also further analyzed LC–MS/MS data using bioinformatic tools. Our study provides valuable information regarding the biology of Ca. Liberibacter species and identifies many putative proteins that may interact with host proteins in the phloem tissues.

Citrus disease detection and classification using based on convolution deep neural network

Plant diseases that cause instability in the food supply reduce agricultural yield and production. As a result, it causes significant economic losses. Citrus, one of the plants, is widely grown all over the world and is used in many fields, especially in nutrition and health. Citrus is an agricultural product of great economic importance worldwide. However, citrus production is severely damaged by pests and various diseases. As a result, serious yield and quality losses are experienced in citrus production. In addition to the reasons stated, more than half of the products are not used in citrus production every year due to different plant diseases and environmental factors.In recent years, depending on the development of technology, image processing and machine learning algorithms have been used in many fields, including agriculture. This provides an opportunity for early detection and classification of plant diseases.In this study, it is aimed to detect and classify blackspot, canker, and greening diseases, which are frequently seen in many different regions, through images. For this purpose, first, preprocessing and segmentation processes are performed on different images from the Citrus Leaves Prepared data set in the literature. Afterward, a unique architecture based on CNN is developed.The developed architecture consists of four blocks and short paths. Each block has convolution, pooling, and batch normalization layers connected in series and parallel. From the test results of the created architecture, the average values of 95%, 96%, 95%, 96% were obtained for F1-score, Precision, Recall, and Accuracy (%) respectively.According to the findings obtained from the study, the proposed model primarily defines citrus black spot, citrus bacterial canker, and citrus disease defined as huanglongbing. It then distinguishes these diseases from each other with high accuracy. The proposed model is presented as a useful decision support tool for citrus growers to recognize and classify citrus diseases.

Identification of citrus diseases based on AMSR and MF-RANet

BackgroundAs one of the most widely planted fruit trees in southern China, citrus occupies an important position in the agriculture field and forestry economy in China. There are many kinds of citrus diseases. If citrus infected with diseases cannot be controlled in time, it easily seriously affects citrus production and causes large economic losses. Timely monitoring of disease characteristics in the citrus growth process is important for implementing timely control measures. Citrus images are easily disturbed by environmental factors such as dust, low light, clouds or leaf shadows. This makes it easy for some disease spot features in citrus pictures to be obscured. Occluded lesions cannot be effectively extracted and recognized. Second, similar characteristics of different diseases also make it difficult to distinguish the different types of diseases. However, the existing machine vision technology for identifying citrus diseases still has some difficulties in dealing with the above problems.ResultsThis paper proposes a new citrus disease identification framework. First, a citrus image enhancement algorithm based on the MSR-AMSR algorithm is proposed, which can enhance the image and highlight the disease characteristic information. The AMSR algorithm can also greatly alleviate the interference of clouds and low light on image lesions, making the image features clearer. Second, an MF-RANet network is proposed to recognize citrus disease images. MF-RANet is composed of a main feature frame and a detail feature frame. The main feature frame uses the cross stacking structure of ResNet50 and RAM to extract the main features in the citrus image dataset. RAM is used to extract the attention weight in the feature layer, which enables RAM to give higher weight to disease features. The detailed feature frame path uses AugFPN to extract features from multiple scales and fuse the main feature frame path. AugFPN enables the network to retain more detailed features, so it can effectively distinguish similar features in different diseases. In addition, we use the ELU activation function not only to solve the problem of gradient explosion and gradient disappearance but also to effectively use the negative input of the network. Finally, we use the label smoothing regularization method to prevent overfitting the network in the classification process. Finally, the experimental results show that the highest detection accuracy of the network for Huanglong disease, Corynespora blight of citrus, fat spot macular disease, citrus scab, citrus canker and healthy citrus is 96.77%, 96.22%, 95.96%, 95.93%, 94.04% and 97.55%, respectively.ConclusionsThe citrus disease algorithm based on AMSR and MF-RANet can effectively perform the disease detection function. It has a high recognition rate for different kinds of citrus diseases. With the addition of AMSR preprocessing, RAM, AugFPN, ELU activation function and other structures, the MF-RANet network performance improves.

Sistem Pakar Diagnosis Hama Penyakit Tanaman Jeruk Keprok Borneo Prima (Citrus Reticulata) Dengan Fuzzy Tsukamoto

Borneo Prima Citrus (Citrus Reticulata) is a monoculture commodity that is susceptible to pests or diseases. The role of an expert is constrained by the fact that expert knowledge is not well documented, the detection process is still manual and expensive, and the distance between agricultural extension workers and plantation sites is far. This problem can be solved by an expert system. The Fuzzy-Tsukamoto method is suitable for solving citrus disease diagnosis problems which have many rules with several symptoms that can represent several diseases. Fuzzy Tsukamoto applies monotonous reasoning to each rule. If in monotonous reasoning, the system only has one rule, in the Tsukamoto method the system consists of several rules. Because it uses the basic concept of monotonous reasoning, in Fuzzy-Tsukamoto's method, every consequence of the IF-THEN rule is represented by a fuzzy set on a monotonic membership function. There were 13 citrus trees indicated to be infected with the disease, which was observed from 500 trees. The accuracy of the results obtained between the expert system and expert knowledge is 84% of 13 diseases and 36 symptoms. The result shows that there are two diseases that are not suitable and 11 diseases according to experts.

Potential of citrus endophyte Bacillus subtilisL1‐21 in the control of Candidatus Liberibacter asiaticus in Asian citrus psyllid, Diaphorina citri

Asian citrus psyllid (ACP), also known as Diaphorina citri, is the natural vector of Candidatus Liberibacter asiaticus (CLas), which is responsible for Huanglongbing (HLB), a devastating citrus disease. Previously, the pathogen was successfully excluded from diseased citrus plants by using the indigenous endophyte Bacillus subtilis L1-21. However, the pathogen elimination and colonization potential of B. subtilis L1-21 in the carrier vector ACP, as well as the recruitment of native microbial communities of psyllid in the presence of endophytes, are still unknown. Initially, we suggested that endophyte L1-21 reduced the CLas copies in ACP from 6.58 × 106 to 5.04 × 104 per insect after 48 h, however, the pathogen copies remained stable in the negative control. The endophyte was stable for 48 h after application. Among the bacterial genera those highlighted in ACP were Candidatus Liberibacter, Pseudomonas, Candidatus Profftella, Methylobacterium-Methylorubrum, Pantoea, Curtobacterium, Wolbachia, Actinomycetospora, and Bacillus. Interestingly, B. subtilis L1-21 easily colonizes the midgut of ACP but cannot be detected in eggs. When ACP with endophyte L1-21 was allowed to feed on new citrus leaves, the highest colonization was observed. We also found that psyllids carrying endophyte L1-21 after feeding on citrus leaves reduced the CLas copies in leaves on the 0, 3rd and 5th day from 8.18 × 10,4 2.6 × 10,3 and 0 pathogen copies/g fresh midvein, respectively. We propose that B. subtilis L1-21 is a native endophyte in citrus and psyllid, which efficiently reduces the CLas pathogen in both citrus and psyllids, provides a more protective effect by increasing the number of cultivable endophytes, and successfully colonizes the midgut of ACP. © 2022 Society of Chemical Industry.

In-Field Citrus Disease Classification via Convolutional Neural Network from Smartphone Images

A high-efficiency, nondestructive, rapid, and automatic crop disease classification method is essential for the modernization of agriculture. To more accurately extract and fit citrus disease image features, we designed a new 13-layer convolutional neural network (CNN13) consisting of multiple convolutional layer stacks and dropout in this study. To address the problem created by the uneven number of disease images in each category, we used the VGG16 network module for transfer learning, which we combined with the proposed CNN13 to form a new joint network, which we called OplusVNet. To verify the performance of the proposed OplusVNet network, we collected 1869 citrus pest and disease images and 202 normal citrus images from the field. The experimental results showed that the proposed OplusVNet can more effectively solve the problem caused by uneven data volume and has higher recognition accuracy, especially for image categories with a relatively small data volume. Compared with the state of the art networks, the generalization ability of the proposed OplusVNet network is stronger for classifying diseases. The classification accuracy of the model prediction results was 0.99, indicating the model can be used as a reference for crop image classification.

Overexpression of the salicylic acid binding protein 2 (SABP2) from tobacco enhances tolerance against Huanglongbing in transgenic citrus.

Overexpression of the salicylic acid binding protein 2 (SABP2) gene from Tobacco results in enhanced tolerance to Huanglongbing (HLB; citrus greening disease) in transgenic sweet oranges. Huanglongbing (HLB), the most destructive citrus disease, is caused by Candidatus Liberibacter asiaticus (CaLas). Currently, no cure for this disease exists, and all commercially planted cultivars are highly susceptible. Salicylic Acid Binding Protein 2 (SABP2) is a well-characterized protein essential for establishing systemic acquired resistance (SAR) in tobacco.The constitutive over expression of SABP2 from tobacco (NtSABP2) in 'Hamlin' sweet orangeresulted in the production of several transgenic lines with variable transcript levels. Transient expression ofthe NtSABP2-EGFP fusion protein in Nicotiana benthamiana plants demonstrated thatNtSABP2was cytosolicin its subcellular localization. In a long-term fieldstudy, we identified a SABP2 transgenic linewithsignificantly reduced HLB symptoms that maintained aconsistently low CaLas titer. Transcriptome analysis of this selectedtransgenic line demonstrated upregulation of several genes related to plant defense and SAR pathways. Genes, such as NPR family genes and those coding for monooxygenases and lipoxygenases, were upregulated in the35S-NtSABP2 overexpressing line and might be candidates for incorporation into our citrus improvement program.

Insight into resistance to 'Candidatus Liberibacter asiaticus,' associated with Huanglongbing, in Oceanian citrus genotypes.

Huanglongbing (HLB), the most destructive citrus disease, is associated with unculturable, phloem-limited Candidatus Liberibacter species, mainly Ca. L. asiaticus (Las). Las is transmitted naturally by the insect Diaphorina citri. In a previous study, we determined that the Oceanian citrus relatives Eremocitrus glauca, Microcitrus warburgiana, Microcitrus papuana, and Microcitrus australis and three hybrids among them and Citrus were full-resistant to Las. After 2 years of evaluations, leaves of those seven genotypes remained Las-free even with their susceptible rootstock being infected. However, Las was detected in their stem bark above the scion-rootstock graft union. Aiming to gain an understanding of the full-resistance phenotype, new experiments were carried out with the challenge-inoculated Oceanian citrus genotypes through which we evaluated: (1) Las acquisition by D. citri fed onto them; (2) Las infection in sweet orange plants grafted with bark or budwood from them; (3) Las infection in sweet orange plants top-grafted onto them; (4) Las infection in new shoots from rooted plants of them; and (5) Las infection in new shoots of them after drastic back-pruning. Overall, results showed that insects that fed on plants from the Oceanian citrus genotypes, their canopies, new flushes, and leaves from rooted cuttings evaluated remained quantitative real-time polymerase chain reaction (qPCR)-negative. Moreover, their budwood pieces were unable to infect sweet orange through grafting. Furthermore, sweet orange control leaves resulted infected when insects fed onto them and graft-receptor susceptible plants. Genomic and morphological analysis of the Oceanian genotypes corroborated that E. glauca and M. warburgiana are pure species while our M. australis accession is an M. australis × M. inodora hybrid and M. papuana is probably a M. papuana × M. warburgiana hybrid. E. glauca × C. sinensis hybrid was found coming from a cross between E. glauca and mandarin or tangor. Eremocitrus × Microcitrus hybrid is a complex admixture of M. australasica, M. australis, and E. glauca while the last hybrid is an M. australasica × M. australis admixture. Confirmation of consistent full resistance in these genotypes with proper validation of their genomic parentages is essential to map properly genomic regions for breeding programs aimed to generate new Citrus-like cultivars yielding immunity to HLB.

FRUIT GROWING IN CHIOS IN THE OTTOMAN EMPIRE

Chios is an island of Greece in the Aegean Sea. The island, which is connected to the North Aegean region of the country, is separated from Turkey by the Strait of Chios. It is famous for mastic trees and mastic gum obtained from these trees. Shortly before the death of Suleiman the Magnificent, the island, which joined the Eyalet of the Archipelago (Eyālet-i Cezāyir-i Baḥr-i Sefīd in Ottoman Turkish) in the Ottoman Empire, was conquered by Kapudan Pasha Piyale Pasha in 1566. Due to the mountainous nature of Chios, a small part of its land is suitable for agriculture. A very small part of the population that made up the island was Muslim. Throughout history, the most important products of the island were mastic, citrus, lemon and orange, almond, pomegranate, olive trees. Citrus products such as fresh lemons, lemon juice, citrus and citron, which constitute the island's most important source of income, were sent to Istanbul to the palace halvahane (Ottoman hist. a section of a sultanic kitchen where desserts and candies were made) under the name "Miri Mübayaa". The material cost of these purchased products was also covered from the mukataa of the island. The problems encountered in the years when products could not be obtained from these trees were also reflected in the documents. In the state yearbooks (salname), a disease called "Kermez " was mentioned, which we cannot get any information about exactly. In the orchards created by two sample farms, it was tried for three years to fight the diseases. This study was conducted by a businesslike science officer. Agriculturalists who became quite skilled in this business were also sent to treat citrus diseases that occurred in other provinces. Olive and the oil obtained from it was one of the pure products of the island. However, the tithe (öşür) tax obtained from olive cultivation, which is available enough to meet the needs of the island, was collected through mukataas and this income was used for the needs of the state, especially the treasury. The main sources of our article are the documents, the state yearbooks, reports and of course travel books in the Ottoman Archives of the Presidency of the State Archives. All kinds of information reflected in the archive about fruit production in Chios and these products, diseases infesting trees, export of fruits, all documents in the archive about trees are explained in detail.