Selection of resistant genotypes of Quince (Cydonia oblonga Mill.) to root and crown rot caused by the pathogen, Phytophthora cactorum, in the lab condition

Garmaroodi, H. Sadeghi; Abdollahi, H.; Garmaroodi, M. Mohammadi

doi:10.1016/j.scienta.2022.111747

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…It has been widely recognized that the deployment of diseaseresistant crop cultivars is the most effective method for disease control, offering both economic and environmental benefits. To date, resistance to P. cactorum has been investigated in a few select crops, specifically strawberry, apple, and most recently quince (Cydonia oblonga; Garmaroodi et al, 2023). genetically complex, allo-octoploid cultivated strawberry (F. × ananassa; 2n = 8× = 56).…”

Section: Resistance In Cropsmentioning

confidence: 99%

The devastating oomycete phytopathogen Phytophthora cactorum: Insights into its biology and molecular features

Chen

Wen

Zhou

et al. 2023

Molecular Plant Pathology

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Phytophthora cactorum is one of the most economically important soilborne oomycete pathogens in the world. It infects more than 200 plant species spanning 54 families, most of which are herbaceous and woody species. Although traditionally considered to be a generalist, marked differences of P. cactorum isolates occur in degree of pathogenicity to different hosts. As the impact of crop loss caused by this species has increased recently, there has been a tremendous increase in the development of new tools, resources, and management strategies to study and combat this devastating pathogen. This review aims to integrate recent molecular biology analyses of P. cactorum with the current knowledge of the cellular and genetic basis of its growth, development, and host infection. The goal is to provide a framework for further studies of P. cactorum by highlighting important biological and molecular features, shedding light on the functions of pathogenicity factors, and developing effective control measures.TaxonomyP. cactorum (Leb. & Cohn) Schröeter: kingdom Chromista; phylum Oomycota; class Oomycetes; order Peronosporales; family Peronosporaceae; genus Phytophthora.Host rangeInfects about 200 plant species in 154 genera representing 54 families. Economically important host plants include strawberry, apple, pear, Panax spp., and walnut.Disease symptomsThe soilborne pathogen often causes root, stem, collar, crown, and fruit rots, as well as foliar infection, stem canker, and seedling damping off.

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Section: Resistance In Cropsmentioning

confidence: 99%

The devastating oomycete phytopathogen Phytophthora cactorum: Insights into its biology and molecular features

Chen

Wen

Zhou

et al. 2023

Molecular Plant Pathology

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Distinguishing Seed Cultivars of Quince (Cydonia oblonga Mill.) Using Models Based on Image Textures Built Using Traditional Machine Learning Algorithms

Ropelewska

Kruczyńska²,

Mieszczakowska-Frąc

2023

Agriculture

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Different cultivars of seeds may have different properties. Therefore, distinguishing cultivars may be important for seed processing and product quality. This study was aimed at revealing the usefulness of innovative models developed based on selected image textures built using traditional machine algorithms for cultivar classification of quince seeds. The quince seeds belonging to four cultivars ‘Uspiech’, ‘Leskovac’, ‘Bereczki’, and ‘Kaszczenko’ were considered. In total, 1629 image textures from different color channels for each seed were extracted from color images acquired using a flatbed scanner. Texture parameters were used to build models for a combined set of selected textures from all color channels, sets of selected textures from color spaces RGB, Lab, and XYZ, and individual color channels R, G, B, L, a, b, X, Y, and Z using algorithms from different groups. The most successful models were developed using the Logistic (group of Functions), IBk (Lazy), LogitBoost (Meta), LMT (Trees), and naïve Bayes (Bayes). The classification accuracy reached 98.75% in the case of a model based on a combined set of textures selected from images in all color channels developed using the Logistic algorithm. For most models, the greatest misclassification of cases was observed between seeds ‘Bereczki’ and ‘Kaszczenko’. The developed procedure can be used in practice to distinguish quince seeds in terms of a cultivar and avoid mixing seed cultivars with different properties intended for further processing.

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Selection of resistant genotypes of Quince (Cydonia oblonga Mill.) to root and crown rot caused by the pathogen, Phytophthora cactorum, in the lab condition

Cited by 2 publications

References 11 publications

The devastating oomycete phytopathogen Phytophthora cactorum: Insights into its biology and molecular features

The devastating oomycete phytopathogen Phytophthora cactorum: Insights into its biology and molecular features

Distinguishing Seed Cultivars of Quince (Cydonia oblonga Mill.) Using Models Based on Image Textures Built Using Traditional Machine Learning Algorithms

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