Bean Leaf Research Articles

New emerging Pseudomonads as causal agents of bean blight disease

Common bean (Phaseolus vulgaris) production is negatively affected by several bacterial plant diseases. A severe outbreak of bean blight was observed in the nine major bean-producing provinces of Iran during 2021–2022. Necrotic spots were observed on leaves which become necrotic with chlorosis beyond it. One hundred-sixty bacterial strains with a greyish-white colony appearance were consistently isolated from the one-hundred eighteen symptomatic bean leaf, pod, seed and root samples on nutrient agar. All strains were screened for their ability to cause disease bean leaves among which 134 strains induced chlorosis three days after inoculation on bean leaves of cv. Yaghoot followed by necrotic spots on the inoculated sites one week after inoculation. The pathogenic strains clustered in three groups based on phenotypic characters and multilocus sequencing analyses. Three housekeeping genes including gyrB, rpoB, and rpoD of representative strains were partially sequenced. In the phylogenetic tree based on concatenated sequences of these three genes or each gene individually, the strains were divided into three clusters each with high bootstrap support. The first group of the strains clustered with Pseudomonas simiae CCUG 50988T. This is the first report of this species as causal agent of bean blight. The second group clustered close to Pseudomonas alloputida NMI5768T which apparently belong to a new species. The third group clustered in a separate clade in Pseudomonas chlororaphis species which belongs to a new subspecies based on pathogenicity on bean, phenotypic characters, sequences of five housekeeping genes which named as subsp. phaseoli subsp. nov. with the type strain UT-M315.

Feeding Preferences of the Bean Leaf Beetle (Ootheca spp.) (Coleoptera: Chrysomelidae): Insights for Targeted Pest Control Strategies in Uganda.

The bean leaf beetle (BLB) (Ootheca spp.) is a polyphagous pest causing significant yield losses in Uganda, particularly in the Northern and Eastern regions on various hosts plants. Despite its polyphagous behaviour, the BLB exhibits preferential feeding, offering an opportunity for targeted pest management. This study explored its feeding preferences across seven crops: common bean, cowpea, greengram, okra, roselle (malakwang), groundnuts, and soybean. This study was conducted in Arua and Lira districts using a randomized complete block design for two rainy seasons (2018A and 2018B). The results showed significant differences in BLB abundance and foliar damage among host crops, locations, days after planting and seasons. Cowpea was the most preferred crop while groundnuts was the least preferred. Therefore, cowpea can be recommended for use as a trap for managing Ootheca spp. in gardens where it is not the main crop. There was a higher pest abundance in Arua than in Lira. There was also a higher pest abundance in 2018A than in 2018B. These findings highlight the importance of understanding BLB's feeding preferences for implementing effective IPM strategies, emphasizing the potential role of trap cropping, especially with cowpea, to minimize BLB damage in resource-constrained agricultural settings.

Identification of mungbean yellow mosaic India virus and susceptibility-related metabolites in the apoplast of mung bean leaves.

The investigation of MYMIV-infected mung bean leaf apoplast revealed viral genome presence, increased EVs secretion, and altered stress-related metabolite composition, providing comprehensive insights into plant-virus interactions. The apoplast, an extracellular space around plant cells, plays a vital role in plant-microbe interactions, influencing signaling, defense, and nutrient transport. While the involvement of apoplast and extracellular vesicles (EVs) in RNA virus infection is documented, the role of the apoplast in plant DNA viruses remains unclear. This study explores the apoplast's role in mungbean yellow mosaic India virus (MYMIV) infection. Our findings demonstrate the presence of MYMIV genomic components in apoplastic fluid, suggesting potential begomovirus cell-to-cell movement via the apoplast. Moreover, MYMIV infection induces increased EVs secretion into the apoplast. NMR-based metabolomics reveals altered metabolic profiles in both apoplast and symplast in response to MYMIV infection, highlighting key metabolites associated with stress and defense mechanisms. The data show an elevation of α- and β-glucose in both apoplast and symplast, suggesting a shift in glucose utilization. Interestingly, this increase in glucose does not contribute to the synthesis of phenolic compounds, potentially influencing the susceptibility of mung bean to MYMIV. Fructose levels increase in the symplast, while apoplastic sucrose levels rise significantly. Symplastic aspartate levels increase, while proline exhibits elevated concentration in the apoplast and reduced concentration in the cytosol, suggesting a role in triggering a hypersensitive response. These findings underscore the critical role of the apoplast in begomovirus infection, providing insights for targeted viral disease management strategies.

Variability in the pod and bean characteristics and their relationship with leaf nutrient content in cocoa (Theobroma cacao L.)

Theobroma cacao L., cocoa, produces fruits throughout the year with one or two peaks. The relationship between pod or bean characteristics and leaf nutrient status in cocoa genotypes grown in the humid tropics of India is not well documented. Our investigation found notable differences amongst twenty genotypes of cocoa for pod weight, husk weight, bean count, fresh bean weight pod, single dry bean weight, nib recovery, and shell percentage. Single dry bean weight is an important parameter of the cocoa beans for the industry, and it was significantly associated with the husk weight, pod weight, and nib recovery. The weight of the dry bean was found to be higher in pods with higher pod weight and husk weight. However, it decreased significantly with increased bean count per pod. Higher nib recovery is another important industry trait and was closely associated with single dry bean weight. Pearson’s correlation coefficients indicated pod and bean characteristics in different genotypes had no significant association with leaf nutrient status. Even, the nutrient ratios had no tangible influence on the pod/bean traits, except for K/P and Ca/B, which had significant associations with the husk weight and pod count per tree, respectively.

Self-Governing Feedback Network (SGFN) Based Super Resolution for bean leaf disease detection

Self-Governing Feedback Network (SGFN) Based Super Resolution for bean leaf disease detection

Potential of Nettle Infusion to Protect Common Bean from Halo Blight Disease

The use of plant-based preparations to replace chemical pesticides is a challenge for sustainable agriculture. Preparations from nettle (Urtica dioica L.) are good candidates, as the treatment of common bean plants (Phaseolus vulgaris L.) with aqueous suspensions of nettle reduced symptoms of halo blight disease caused by Pseudomonas syringae pv. phaseolicola (Pph). The aim of the present work was to test if nettle infusions (Ui) have similar effects and find out what activity is behind this: antimicrobial, promotion of the natural defenses of plants, and/or antioxidant. To achieve this, Pph growth was tested in the presence of infusions of nettle leaves collected in two different years (Ui18 and Ui22), and we found that it was only weakly inhibited at high concentrations of Ui18. Interestingly, Ui22 promoted bacteria growth at all concentrations. Second, we estimated the production of reactive oxygen species (ROS) in response to flagellin22 (flg22) in common bean leaf discs, since recognition of this bacterial peptide usually leads to ROS accumulation in tissues as a plant immune response. However, leaf discs that were preincubated in Ui showed no accumulation of ROS after flg22 treatment, suggesting that Ui can neutralize ROS production. Finally, in a Pph inoculation experiment of common bean plants grown in vitro, we observed that pretreatment of plants with Ui drastically reduced foliar oxidative damage and disease symptoms 6 h after inoculation. This effect was more noticeable for Ui22, which was related to the higher antioxidant activity found in this extract in comparison with Ui18. These results suggest that the protective properties of Ui are mainly due to the content of antioxidant bioactive compounds.

Efficient Faba Bean Leaf Disease Identification through Smart Detection using Deep Convolutional Neural Networks

Background: Legumes, such as lentils, field peas, Faba beans and chickpeas, are high in vitamins, fiber, important minerals and protein and can help avoid obesity and cardiovascular illnesses. They also contribute to ecosystem services, such as nitrogen fixation and resilience to environmental stresses. Despite a 60% increase in global pulse production from 2000 to 2021, a demand-supply gap, especially in South Asia, raises concerns about nutritional access. Since illnesses are currently an issue to the food security of faba beans, machine learning is required for efficient disease identification. Methods: This research employs Convolutional Neural Networks (CNNs) for robust Faba bean leaf disease identification. The CNN model is trained with diverse images representing specific diseases. The study focuses on diseases like Chocolate Spot, Faba Bean Gall, Rust and Healthy leaves. Image processing involves resizing, grayscale conversion and labeling. The CNN architecture includes eight convolutional layers, four max-pooling layers and three dropout layers. The model is trained using 80% of the dataset, validated using 20% and tested for accuracy. Result: The CNN model achieves an accuracy of 99.37% during training and 89.69% during validation after 75 epochs. Confusion matrix and classification report illustrate the model’s performance. It shows high precision, recall and F1 scores for each class, indicating balanced performance. Chocolate Spot and Rust exhibit the highest precision and F1 scores. The overall accuracy is 91%, comparable to other studies on Faba bean disease detection. The study presents a CNN-based disease identification system for Faba beans, demonstrating high accuracy and balanced performance across different diseases. The model’s effectiveness is comparable to other advanced techniques. The research highlights the potential of machine learning in optimizing disease management for Faba beans. Future work could explore a broader range of diseases and incorporate hybrid machine learning algorithms for further improvement.

Exploring Ethnomathematics on The Batik Patterns of Jember In Mathematics Concept

This study aims to identify and describe mathematical concepts and describe the results of ethnomathematics exploration contained in Jember batik motifs. The type of research used in this study is qualitative research with an ethnographic approach. The research instruments used were observation, tests, and interviews with batik artisans. The results of this study indicate that the combination of coffee bean and tobacco leaf motifs has applied mathematical concepts of geometry. In the motif of coffee beans and tobacco leaves from the Nhora Pangetu hand-written batik production house, there are mathematical concepts of curves, points, circles, and the concept of dilation. The motif of coffee beans and tobacco leaves from the Gangsar Ngaidin batik production house contains mathematical concepts of reflection (mirroring), congruence, and flat shapes of circles, triangles, and rhombuses.

Functional response of Franklinothrips vespiformis (Thysanoptera: Aeolothripidae) to eggs and nymphs of Bemisia tabaci (Hemiptera: Aleyrodidae).

The Middle East Asia Minor 1 biotype of Bemisia tabaci Gennadius (Hemiptera: Aleyrodidae) is a greenhouse and field crop pest of global significance. The objective of this study was to assess the potential of the generalist predatory thrips, Franklinothrips vespiformis Crawford (Thysanoptera: Aeolothripidae), as a biological control agent for B. tabaci. This was achieved by determining the functional responses of F. vespiformis larvae and adults to the egg and nymphal stages of B. tabaci under laboratory conditions. Analyses consisted of 10 replicates of each predator and prey stage combination on bean leaf discs for a 24-h period. Following logistic regression analyses to determine the functional response type exhibited, response parameters were estimated with nonlinear least squares regression using Roger's equation. Results showed that F. vespiformis larvae and adults exhibited a Type II functional response when feeding on immature B. tabaci. The handling times (Th) of F. vespiformis larvae and adults were magnitudes higher for B. tabaci nymphs than they were for eggs, which were in part driven by the higher attack rates (a) observed on eggs. The maximum attack rate (T/Th) for B. tabaci eggs and nymphs exhibited by first-stage larvae, second-stage larvae, and adult F. vespiformis increased with increasing predator age. Results from this study suggest that F. vespiformis larvae and particularly adults are promising biological control agents for B. tabaci and are efficient predators at both low and high prey densities.

Could Aqueous Extract from Castor Plants be the Solution to Effectively Control the Pink Mealybug Nymphs?

Objective: To evaluate under laboratory conditions the potential of the leaf extract of the species Ricinus communis L. regarding its insecticidal activity on nymphs of the pink mealybug and determine the Lethal Concentration 50 (LC50). Theoretical reference: Maconellicoccus hirsutus is one of the key pests of several crops and has been diagnosed in more than 350 plant species. The species was reported for the first time in Brazil in 2010 in the State of Roraima, and has since spread to other Brazilian states causing considerable economic losses to the affected areas. Method: The castor bean leaf extract was tested on M. hirsutus insects with varying concentrations. Application was done directly using an airbrush connected to a calibrated compressor. Evaluation occurred at 24, 48, and 72-hour intervals post-application. Results and discussion: As the dosage of the extract was increased, a significant increase in the mortality rate of mealybugs was observed. The lethal concentration for 50 % of the M. hirsutus population (LC50) was calculated at 6.14%, with a confidence interval between 5.40 % and 7.02 % (m/v). Implications of the research: The research proposes a sustainable approach to controlling pink mealybug nymphs, using an aqueous extract of R. communis leaves. Originality/value: This article stands out for its innovation in exploring a sustainable approach to pest control, presenting an effective alternative to reduce dependence on chemical pesticides. This proposal is especially relevant to promote the adoption of more sustainable agricultural practices aligned with the principles of sustainable agriculture and providing benefits for both agricultural production and the environment.

Utilizing Remote Sensing to Quantify the Performance of Soybean Insecticide Seed Treatments

Soybean (Glycine max) is one of the most important oilseed crops grown in North America and a key contributor to the global protein supply. Insect feeding by a major soybean pest, the bean leaf beetle (BLB; Cerotoma trifurcata), can result in economic yield loss if not controlled. The objective of this research was to use unmanned aerial vehicle (UAV) image analysis to compare the agronomic and efficacy traits of two soybean insecticide seed treatments (IST) in locations with BLB feeding. Across the 2018–2023 field trial locations, 29 had low BLB feeding pressure (less than 25% feeding damage to no IST plots) and 31 had high BLB feeding pressure (greater than 25% feeding damage to no IST plots). In low BLB pressure locations, cyantraniliprole and imidacloprid seed treatments had significantly higher BLB efficacy, significantly higher UAV greenness, and significantly higher final yield as compared to no IST. In high BLB pressure locations, cyantraniliprole and imidacloprid seed treatments were significantly better compared to no IST for BLB efficacy, UAV emergence, UAV vigor, UAV greenness, and final yield. In high BLB pressure locations, cyantraniliprole had significantly higher BLB efficacy, significantly better UAV emergence, and significantly higher yield compared to imidacloprid. The cyantraniliprole treatment had a +254.5 kg/ha increase compared to no IST in low BLB pressure locations and a +213.7 kg/ha increase in high BLB pressure locations. The imidacloprid treatment had a +163.4 kg/ha yield increase compared to no IST in low BLB pressure locations and a +121.4 kg/ha yield increase in high BLB pressure locations. The use of UAV image analysis enabled quantification of the effect of BLB feeding on early-season agronomic traits and, when combined with efficacy and final yield data, successfully differentiated the performance of two soybean ISTs in environments with low or high insect pressure.

Ingestion of Bean Leaves Reduces Metabolic Complications and Restores Intestinal Integrity in C57BL/6 Mice with Obesity Induced by a High-Fat and High-Fructose Diet.

Consumption of foods with fiber and compounds can promote gastrointestinal health and reduce obesity complications. Therefore, treatment with common bean leaves (BL) against obesity was evaluated in mice with a high-fat and high-fructose diet (HFFD) for 14 weeks. The bromatological and phytochemical characterization of BL were determined. Afterwards, the animals were supplemented with BL (10%) or a standard diet (SD) as a strategy to encourage a healthy diet for 12 additional weeks. Changes in body composition, lipid profile, and intestinal integrity were analyzed. The characterization of BL stood out for its content of 27.2% dietary fiber, total phenolics (475.04 mg/100 g), and saponins (2.2 mg/100 g). The visceral adipose tissue (VAT) decreased in the BL group by 52% compared to the HFFD group. Additionally, triglyceride levels were 23% lower in the BL consumption group compared to the HFFD group. The improvement in lipid profile was attributed to the 1.77-fold higher fecal lipid excretion in the BL consumption group compared to the HFFD group and the inhibition of pancreatic lipase by 29%. Furthermore, BL supplementation reduced the serum levels of IL-6 (4.4-fold) and FITC-dextran by 50% compared with those in the HFFD group. Metabolic endotoxemia was inhibited after BL supplementation (-33%) compared to the HFFD group. BL consumption as a treatment in obese mice reduces adipose tissue accumulation and improves the lipid profile. Furthermore, we report for the first time that BL consumption improves intestinal integrity.

Measurement of O2 Uptake and Evolution in Leaves In Vivo Using Stable Isotopes and Membrane Inlet Mass Spectrometry.

Oxygen is both product and substrate of photosynthesis and metabolism in plants, by oxygen evolution through water splitting and uptake by photorespiration and respiration. It is important to investigate these processes simultaneously in leaves, especially in response to environmental variables, such as light and temperature. To distinguish between processes that evolve or take up O2 in leaves in the light, in vivo gas exchange of stable isotopes of oxygen and membrane inlet mass spectrometry is used. A closed-cuvette system for gas exchange of leaf discs is described, using the stable isotopes 16O2 and 18O2, with a semi-permeable membrane gas inlet and isotope mass separation and detection by mass spectrometry. Measurement of evolution and uptake, as well as CO2 uptake, at a range of light levels allows composition of a light response curve, here described for French bean (Phaseolus vulgaris) and maize (Zea mays) leaf discs.

Disease detection in bean leaves using deep learning

The care and health of agricultural plants, which are the primary source for people to eat healthily, are essential. Disease detection in plants is one of the critical elements of smart agriculture. In parallel with the development of artificial intelligence, advancements in smart agriculture are also progressing. The development of deep learning techniques positively affects smart farming practices. Today, using deep learning and computer vision techniques, various plant diseases can be detected from images such as photographs. In this research, deep learning techniques were used to detect and diagnose bean leaf diseases. Healthy and diseased bean leaf images were used to train the convolutional neural network (CNN) model, which is one of the deep learning techniques. Transfer learning was applied to CNN models to detect plant diseases with the difference of related works. A transfer learning-based strategy to identify various diseases in plant varieties is demonstrated using leaf images of healthy and diseased plants from the Bean dataset. With the proposed method, 1295 images were studied. The results show that our technique successfully identified disease status in bean leaf images, achieving an accuracy of 98.33% with the ResNet50 model.

Nigrospora musae and N. oryzae as new causal agents of broad bean leaf spot disease in China

Broad bean (Vicia faba L.) is a cold-season legume crop, planted worldwide for its superior nutritional value and important role in sustainable agriculture. Leaf spot is one of the major diseases on broad bean leaves, stems and pods, the infections will result in severe yield reduction. In 2021, a leaf spot disease was extensively observed on broad bean plants in Longli County and Guiyang, Zunyi and Liupanshui Cities, Guizhou Province, China. Based on morphology and phylogenetic analysis using the ribosomal internal transcribed spacer region (ITS), 28S rRNA gene (LSU), β-tubulin (TUB2) and translation elongation factor 1-alpha (TEF1-α) gene sequences, the 12 isolates in this study represent two species of Nigrospora, namely N. musae, and N. oryzae. Moreover, the isolates of the two Nigrospora species found in the survey were tested for pathogenicity and indicated that they were all pathogenic to the broad bean plants. Same pathogens were successfully reisolated from the typical leaf spot lesions. Koch's postulates for N. musae and N. oryzae were fulfilled in this study. Thus, this study confirms species of Nigrospora as causal agents of leaf spot disease on broad bean in China, which provides a basis for subsequent research to develop effective preventative measures.

Field evaluation of Bt 127SC formulation for efficacy against Lepidopteran larvae infesting soybean under Manipur conditions

Bt 127 SC formulation supplied by the Indian Institute of Oilseeds Research, Hyderabad was field tested at Imphal (Manipur) during kharif 2018 - 2020 against lepidopteran larvae infesting soybean.Two spray applications of Bt 127 SC formulation @ 3 ml/litre of water was observed to be less superior to commercial synthetic insecticides but at par with Bt commercial delfin in reducing bihar hairy caterpillar (BHC) and bean leaf webber and comparatively similar in efficacy with the synthetic insecticides in reducing tobacco caterpillar.

Comprehensive biochemical approach forunderstanding the interaction between host "common bean" and pathogen "Colletotrichum lindemuthianum" causing bean anthracnose.

Anthracnose (ANT) caused by Colletotrichum lindemuthianum is the most devastating seed-borne fungal disease of common bean. In response to fungal infections, it is hypothesized that pathogen-plant interactions typically cause hypersensitive reactions by producing reactive oxygen species, hydrogen peroxide and lipid peroxidation of cell membranes. esent study was conducted by inoculating susceptible bean genotype "SB174" and resistant bean genotype "E10" with pathogen "C. lindemuthianum". Defense-related enzymes (ascorbate peroxidase, peroxidase, lipid peroxidase, and catalase) and C-based compounds (total phenols and flavonoids) were studied using the detached bean leaf method. Comparative defense response was studied in different plant tissues (pod, stem, and seed) in susceptible and resistant bean genotypes under uninoculated and pathogen-inoculated conditions. The host‒pathogen interaction was studied at mock inoculation, 2, 4 and 6 days after inoculation (dai). Comparing the pathogen-inoculated bean leaves to water-treated bean leaves, defense enzymes as well as total phenols and flavonoids exhibited differential expression. In a comparative study, the enzyme activity also displayed differential biochemical responses in pods, stems and seeds in both contrasting genotypes. For example, 5.1-fold (pod), 1.5-fold (stem) and 1.06-fold (seed) increases in ascorbate peroxidase activity were observed in the susceptible genotype at 6 dai compared to mock inoculation. Similarly, catalase activity in pods was upregulated (1.47-fold) in the resistant genotype and downregulated (1.30-fold) in the susceptible genotype at 6 dai. The study revealed that defense-related antioxidative enzymes, phenols and flavonoids are fine-tuned to detoxify important reactive oxygen species (ROS) molecules, induce systemic resistance and are successfully controlled in common bean plants against pathogen invasion.

Recognizing the Types of Beans Using Artificial Intelligence

Many studies have previously addressed the recognition of plant leaf types. The process of identifying these leaf types involves a crucial feature extraction stage. Image feature extraction is pivotal for distinguishing the types of objects, thus demanding optimal feature analysis for accurate leaf type determination. Prior research, which employed the CNN method, faced challenges in effectively distinguishing between long bean and green bean leaves when identifying bean leaves. Therefore, there is a need to conduct optimal feature analysis to correctly classify bean leaves. In our research, we analyzed 69 features and explored their correlations within various image types, including RGB, L*a*b, HSV, grayscale, and binary images. The primary objective of this study is to pinpoint the features most strongly correlated with the recognition of bean leaf types, specifically green bean, soybeans, long beans, and peanuts. Our dataset, sourced from farmers' fields and verified by experienced senior farmers, consists of 456 images. The most highly correlated feature within the bean leaf image category is STD b in the L*a*b image. Furthermore, the most effective method for leaf type recognition is Neural Network Backpropagation, achieving an accuracy rate of 82.28% when applied to HSV images.

Detection and Practical Differentiation of Phytoplasmas from Several Host Plants Using PCR-RFLP

Phytoplasma as a phytopathogenic prokaryote with a wide host range is a pathogen that needs more attention in Indonesia. This pathogen is relatively difficult to detect and identify due to its complicated biological properties. This study involved detection of phytoplasmas by polymerase chain reaction (PCR) technique with P1/P7 primers from seven symptomatic plants, i.e. Bermuda grass white leaf, bamboo yellows, witches’ broom of peanut, soybean, yard long bean, and cactus, and sweet potato little leaf. The phytoplasma DNA of the 16S rRNA gene resulting from PCR amplification was examined by digestion reaction using three endonuclease enzymes AluI, RSaI, and MSeI to generate restriction fragment length polymorphism (RFLP) profile. The seven diseased plants were confirmed positive to be associated with phytoplasma as indicated by the PCR product of 1800 bp. Based on the RFLP profiles of the three enzymes, the phytoplasmas were divided into two groups, namely group I (Bermuda grass and bamboo) and group II (peanuts, soybeans, yard long beans, cactus, and sweet potatoes). Cactus phytoplasma is a sub-group (strain) because it has a slightly different fragment of MSeI RFLP profile.

Association of a novel begomovirus species with fenugreek yellow vein disease in India.

Fenugreek (Trigonella foenum-graecumL.) is an annual medicinal and spice crop belonging to the family Fabaceae. The occurrence of a yellow vein disease was recorded in fenugreek in Jodhpur (India) in 2022. The infection of begomoviruses in legume crops results in significant yield loss and major economic loss. The current study reports an association of a novel begomovirus species associated with yellow vein disease in Fenugreek. In symptomatic fenugreek plants, geminivirus-like particles were visible under a transmission electron microscope. Further, nucleotide sequence analysis of the rolling circle amplified product revealed 2743 nucleotide DNA-A genome with close relatedness to French bean leaf curl virus (88.21%) and Senna leaf curl virus (87.63%). It was proposed as a new begomovirus species, Fenugreek yellow vein Rajasthan virus. The genome organization suggested the presence of a typical nonanucleotide sequence along with 7 ORFs in DNA-A. A possible recombination event took place in the coat protein (V1) region with Pedilanthus leaf curl virus and Chilli leaf curl virus as major and minor parents. The recombinant virus poses possible threats to several other legume crops. To the best of our knowledge, this is the first report of the association of FeYVRaV with fenugreek yellow vein disease from northwestern India. In conclusion, the presence of a novel begomovirus species associated with yellow vein disease in fenugreek is alarming and needs further studies on its infectivity to prevent its spread to legume crops.