Your browser doesn't support javascript.
loading
Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight-related traits in winter wheat.
Thapa, Subash; Gill, Harsimardeep S; Halder, Jyotirmoy; Rana, Anshul; Ali, Shaukat; Maimaitijiang, Maitiniyazi; Gill, Upinder; Bernardo, Amy; St Amand, Paul; Bai, Guihua; Sehgal, Sunish K.
Afiliación
  • Thapa S; Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA.
  • Gill HS; Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA.
  • Halder J; Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA.
  • Rana A; Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA.
  • Ali S; Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA.
  • Maimaitijiang M; Department of Geography & Geospatial Sciences, Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, South Dakota, USA.
  • Gill U; Department of Plant Pathology, North Dakota State University, Fargo, North Dakota, USA.
  • Bernardo A; USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, Kansas, USA.
  • St Amand P; USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, Kansas, USA.
  • Bai G; USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, Kansas, USA.
  • Sehgal SK; Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA.
Plant Genome ; : e20470, 2024 Jun 09.
Article en En | MEDLINE | ID: mdl-38853339
ABSTRACT
Fusarium head blight (FHB) remains one of the most destructive diseases of wheat (Triticum aestivum L.), causing considerable losses in yield and end-use quality. Phenotyping of FHB resistance traits, Fusarium-damaged kernels (FDK), and deoxynivalenol (DON), is either prone to human biases or resource expensive, hindering the progress in breeding for FHB-resistant cultivars. Though genomic selection (GS) can be an effective way to select these traits, inaccurate phenotyping remains a hurdle in exploiting this approach. Here, we used an artificial intelligence (AI)-based precise FDK estimation that exhibits high heritability and correlation with DON. Further, GS using AI-based FDK (FDK_QVIS/FDK_QNIR) showed a two-fold increase in predictive ability (PA) compared to GS for traditionally estimated FDK (FDK_V). Next, the AI-based FDK was evaluated along with other traits in multi-trait (MT) GS models to predict DON. The inclusion of FDK_QNIR and FDK_QVIS with days to heading as covariates improved the PA for DON by 58% over the baseline single-trait GS model. We next used hyperspectral imaging of FHB-infected wheat kernels as a novel avenue to improve the MT GS for DON. The PA for DON using selected wavebands derived from hyperspectral imaging in MT GS models surpassed the single-trait GS model by around 40%. Finally, we evaluated phenomic prediction for DON by integrating hyperspectral imaging with deep learning to directly predict DON in FHB-infected wheat kernels and observed an accuracy (R2 = 0.45) comparable to best-performing MT GS models. This study demonstrates the potential application of AI and vision-based platforms to improve PA for FHB-related traits using genomic and phenomic selection.
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Plant Genome Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Plant Genome Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos