Resistance to rusts (Uromyces pisi and U. viciae-fabae) in pea

Barilli, Eleonora; Sillero, Josefina C.; Prats, Elena; Rubiales, Diego

doi:10.17221/125/2013-cjgpb

Cited by 16 publications

(10 citation statements)

References 55 publications

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“…The fungal class Pucciniomycetes (formerly known as Urediniomycetes) of the Basidiomycota phyla contains most plant pathogens that cause fungal rust (Swann et al 2001). In this study, we found the presence of the wheat leaf rust fungi Puccinia recondita and faba-beans rust fungi Uromyces viciae-fabae (Barilli et al 2014) (belonging to the fungal class Pucciniomycete) in the bioaerosol samples. Hence, it was evident that the wheat eld in the study region was susceptible to brown leaf rust infection.…”

Section: Discussionmentioning

confidence: 58%

Relationship between fungal bioaerosols and biotic stress on crops: a case study on wheat rust fungi

Varghese,

Krishnamoorthy,

Patel

et al. 2024

J Plant Dis Prot

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This study proposes the relationship between fungal bioaerosols and biotic stress on crops using a case study on wheat leaf rust reported from northern India. We sampled and quanti ed the size-resolved fungal bioaerosols using the Next-Generation Sequencing technique from a wheat crop eld during winter. Puccinia recondita, the fungal pathogen that causes wheat leaf rust, was identi ed during the study period. The pathogen is known for its frequent and widespread occurrence of new variants that causes disease-resistant crop varieties susceptible to infections. This reveals the need for frequent and systematic monitoring to prevent rust infection. In the current study, the size-resolved fungal bioaerosol characterisation was linked to the dispersal properties of the fungal propagules, and using a theoretical dispersion model, originating source and the areas of high risk for wheat leaf rust infection were identi ed. Our ndings may serve as a vital reference for crop pathologists, agro technologists, environmentalists, and policymakers to expand the investigation on the biotic stress caused by the invasion of fungal bioaerosols on various crops and to implement preventive measures to ensure global food security.

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Section: Discussionmentioning

confidence: 58%

Relationship between fungal bioaerosols and biotic stress on crops: a case study on wheat rust fungi

Varghese,

Krishnamoorthy,

Patel

et al. 2024

J Plant Dis Prot

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“…Infecting plants produce pseudo flowers, similar to true flowers in colour and shape and also leaves at the top of stem appear in rosette pattern (PFUNDER and ROY, 2000). Many authors have described rust symptoms on pea (BARILLI et al, 2009;BARILLI et al, 2014;KUSHWAHA et al, 2006;KUSHWAHA et al, 2010;CHAND et al, 2006;XUE et al, 2002). In pea plant, early rust symptoms develop on abaxial side of older leaves and form round to oval aecidia.…”

Section: Rustmentioning

confidence: 99%

An overview of rust (Uromyces viciae-fabae) and powdery mildew (Erysiphe polygoni DC) of pea (Pisum sativum L.)

et al. 2022

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Pea is a self-pollinating, cool season leguminous crop with a diploid chromosome number of 14. Pea is cultivated extensively and because of high protein content, pea is a crop with great significance. However, cultivation of pea gets affected by numerous biotic and abiotic stresses. Fungal diseases such as rust, powdery mildew, fusarium wilt etc. comes under the biotic stresses which are most widespread. Rust and powdery mildew cause major damage to the crop in both tropical and temperate locales of the world. Use of fungicide to control plant diseases is a good approach but excessive use of fungicide can cause environmental pollution and disasters throughout the world and can also built resistance in the pathogens. Therefore, to remove these constraints, disease resistant varieties must be used. Use of resistant varieties is a safe and efficient alternative method to control plant diseases. Breeding for rust and powdery mildew resistance has been started globally and a number of resistant sources have been identified. To introgress resistant gene into commercial varieties of pea, molecular tools must be integrated with conventional breeding techniques. Till date only one linkage map has been generated for rust resistance in pea; while for powdery mildew, three genes have been mapped. Molecular markers linked to these genes can be used in breeding programs of resistance varieties. To improve the efficiency of selection for rust and powdery mildew resistance and enhance varietal development, the integrated approach of genomic resources, effective molecular tools and high resolution phenotyping tools must be used. An overview of pea rust and powdery mildew, pathogen structure, yield losses and breeding techniques implied to control these diseases, is provided in this review article.

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“…Despite being widely used, visual DS estimations can be imprecise and biased for diseases with small and numerous lesions like rust [ 25 ]. Therefore, researchers usually also analyse IF and/or PS that are less prone to user bias to quantify more precisely partial resistance in pea [ 71 ]. Some previous studies reported the estimation of some of these disease components through RGB image analysis with variable efficiency [ 38 , 62 , 72 , 73 ].…”

Section: Processing Time Optimizationmentioning

confidence: 99%

RGB image-based method for phenotyping rust disease progress in pea leaves using R

Osuna-Caballero,

Olivoto,

Jiménez-Vaquero

et al. 2023

Plant Methods

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Background Rust is a damaging disease affecting vital crops, including pea, and identifying highly resistant genotypes remains a challenge. Accurate measurement of infection levels in large germplasm collections is crucial for finding new resistance sources. Current evaluation methods rely on visual estimation of disease severity and infection type under field or controlled conditions. While they identify some resistance sources, they are error-prone and time-consuming. An image analysis system proves useful, providing an easy-to-use and affordable way to quickly count and measure rust-induced pustules on pea samples. This study aimed to develop an automated image analysis pipeline for accurately calculating rust disease progression parameters under controlled conditions, ensuring reliable data collection. Results A highly efficient and automatic image-based method for assessing rust disease in pea leaves was developed using R. The method’s optimization and validation involved testing different segmentation indices and image resolutions on 600 pea leaflets with rust symptoms. The approach allows automatic estimation of parameters like pustule number, pustule size, leaf area, and percentage of pustule coverage. It reconstructs time series data for each leaf and integrates daily estimates into disease progression parameters, including latency period and area under the disease progression curve. Significant variation in disease responses was observed between genotypes using both visual ratings and image-based analysis. Among assessed segmentation indices, the Normalized Green Red Difference Index (NGRDI) proved fastest, analysing 600 leaflets at 60% resolution in 62 s with parallel processing. Lin’s concordance correlation coefficient between image-based and visual pustule counting showed over 0.98 accuracy at full resolution. While lower resolution slightly reduced accuracy, differences were statistically insignificant for most disease progression parameters, significantly reducing processing time and storage space. NGRDI was optimal at all time points, providing highly accurate estimations with minimal accumulated error. Conclusions A new image-based method for monitoring pea rust disease in detached leaves, using RGB spectral indices segmentation and pixel value thresholding, improves resolution and precision. It rapidly analyses hundreds of images with accuracy comparable to visual methods and higher than other image-based approaches. This method evaluates rust progression in pea, eliminating rater-induced errors from traditional methods. Implementing this approach to evaluate large germplasm collections will improve our understanding of plant-pathogen interactions and aid future breeding for novel pea cultivars with increased rust resistance.

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Resistance to rusts (Uromyces pisi and U. viciae-fabae) in pea

Cited by 16 publications

References 55 publications

Relationship between fungal bioaerosols and biotic stress on crops: a case study on wheat rust fungi

Relationship between fungal bioaerosols and biotic stress on crops: a case study on wheat rust fungi

An overview of rust (Uromyces viciae-fabae) and powdery mildew (Erysiphe polygoni DC) of pea (Pisum sativum L.)

RGB image-based method for phenotyping rust disease progress in pea leaves using R

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