Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight–related traits in winter wheat
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...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-09-01
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| Series: | The Plant Genome |
| Online Access: | https://doi.org/10.1002/tpg2.20470 |
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| author | Subash Thapa Harsimardeep S. Gill Jyotirmoy Halder Anshul Rana Shaukat Ali Maitiniyazi Maimaitijiang Upinder Gill Amy Bernardo Paul St. Amand Guihua Bai Sunish K. Sehgal |
| author_facet | Subash Thapa Harsimardeep S. Gill Jyotirmoy Halder Anshul Rana Shaukat Ali Maitiniyazi Maimaitijiang Upinder Gill Amy Bernardo Paul St. Amand Guihua Bai Sunish K. Sehgal |
| author_sort | Subash Thapa |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-34f2080201ee4cc5883812080a9dfd18 |
| institution | Kabale University |
| issn | 1940-3372 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Plant Genome |
| spelling | doaj-art-34f2080201ee4cc5883812080a9dfd182024-11-17T09:46:07ZengWileyThe Plant Genome1940-33722024-09-01173n/an/a10.1002/tpg2.20470Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight–related traits in winter wheatSubash Thapa0Harsimardeep S. Gill1Jyotirmoy Halder2Anshul Rana3Shaukat Ali4Maitiniyazi Maimaitijiang5Upinder Gill6Amy Bernardo7Paul St. Amand8Guihua Bai9Sunish K. Sehgal10Department of Agronomy, Horticulture and Plant Science South Dakota State University Brookings South Dakota USADepartment of Agronomy, Horticulture and Plant Science South Dakota State University Brookings South Dakota USADepartment of Agronomy, Horticulture and Plant Science South Dakota State University Brookings South Dakota USADepartment of Agronomy, Horticulture and Plant Science South Dakota State University Brookings South Dakota USADepartment of Agronomy, Horticulture and Plant Science South Dakota State University Brookings South Dakota USADepartment of Geography & Geospatial Sciences, Geospatial Sciences Center of Excellence South Dakota State University Brookings South Dakota USADepartment of Plant Pathology North Dakota State University Fargo North Dakota USAUSDA‐ARS, Hard Winter Wheat Genetics Research Unit Manhattan Kansas USAUSDA‐ARS, Hard Winter Wheat Genetics Research Unit Manhattan Kansas USAUSDA‐ARS, Hard Winter Wheat Genetics Research Unit Manhattan Kansas USADepartment of Agronomy, Horticulture and Plant Science South Dakota State University Brookings South Dakota USAAbstract 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.https://doi.org/10.1002/tpg2.20470 |
| spellingShingle | Subash Thapa Harsimardeep S. Gill Jyotirmoy Halder Anshul Rana Shaukat Ali Maitiniyazi Maimaitijiang Upinder Gill Amy Bernardo Paul St. Amand Guihua Bai Sunish K. Sehgal Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight–related traits in winter wheat The Plant Genome |
| title | Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight–related traits in winter wheat |
| title_full | Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight–related traits in winter wheat |
| title_fullStr | Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight–related traits in winter wheat |
| title_full_unstemmed | Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight–related traits in winter wheat |
| title_short | Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight–related traits in winter wheat |
| title_sort | integrating genomics phenomics and deep learning improves the predictive ability for fusarium head blight related traits in winter wheat |
| url | https://doi.org/10.1002/tpg2.20470 |
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