Skip to main content
SLU:s publikationsdatabas (SLUpub)

Forskningsartikel2023Vetenskapligt granskadÖppen tillgång

Nymphstar: An accurate high-throughput quantitative methodfor whitefly (Aleurotrachelus socialis Bondar) resistancephenotyping in cassava

Bohorquez-Chaux, Adriana; Gómez-Jiménez, María Isabel; Leiva-Sandoval, Luisa Fernanda; et al.


Whitefly (Aleurotrachelus socialisBondar) is a major pest causing significant eco-nomic losses in cassava production systems in North South America. It diminishescassava’s photosynthesis by colonizing leaves, directly feeding on phloem sap, orexcreting substances that foster sooty mold growth, reducing the photosynthetic area.The most effective pest management approach is deploying natural resistance in thecrop. Identifying germplasm with superior whitefly-resistance (WFR) through phe-notypic evaluation distinguishing it from whitefly-susceptible responses requires anaccurate, high-throughput, quantitative phenotyping method. We developed Nymph-star, an image-based phenotyping tool, as an ImageJ plugin, quantifying third- andfourth-instar nymphs and their leaf area they occupy through red, green, and bluecolor space analysis. Using Nymphstar, we tested 19 cassava genotypes and classifiedtheir resistance toA. socialis. The plugin proved efficient, completing the analysisin 25.56 min on average for the entire dataset. In contrast, manual counting for thesame set of images took 425.23 min on average averaging around 6.29 min/image. Nymphstar was∼17 times faster showcasing its efficiency. To assess WFR in cas-sava germplasm, we conducted a full-bench caging free-choice assay. This approachenhanced whitefly colonization on each cassava genotype, providing an accurate rep-resentation of resistance/susceptible while reducing operator bias. Nymphstar is arapid, precise tool for automated nymphs counting and leaf area quantification. Itfacilitates the large-scale assessment of cassava resistance to whitefly, eliminatingbias associated with field assessment and manual countin

Publicerad i

The Plant Phenome Journal
2023, Volym: 6, nummer: 1, artikelnummer: e20089

    Associerade SLU-program

    SLU Nätverk växtskydd

    UKÄ forskningsämne


    Publikationens identifierare


    Permanent länk till denna sida (URI)