Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning
Abstract Background High-throughput plant phenotyping (HTPP) methods have the potential to speed up the crop breeding process through the development of cost-effective, rapid and scalable phenotyping methods amenable to automation. Crop disease resistance breeding stands to benefit from successful i...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-03-01
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| Series: | Plant Methods |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13007-022-00868-0 |
| _version_ | 1818321299650379776 |
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| author | Alexander Koc Firuz Odilbekov Marwan Alamrani Tina Henriksson Aakash Chawade |
| author_facet | Alexander Koc Firuz Odilbekov Marwan Alamrani Tina Henriksson Aakash Chawade |
| author_sort | Alexander Koc |
| collection | DOAJ |
| description | Abstract Background High-throughput plant phenotyping (HTPP) methods have the potential to speed up the crop breeding process through the development of cost-effective, rapid and scalable phenotyping methods amenable to automation. Crop disease resistance breeding stands to benefit from successful implementation of HTPP methods, as bypassing the bottleneck posed by traditional visual phenotyping of disease, enables the screening of larger and more diverse populations for novel sources of resistance. The aim of this study was to use HTPP data obtained through proximal phenotyping to predict yellow rust scores in a large winter wheat field trial. Results The results show that 40–42 spectral vegetation indices (SVIs) derived from spectroradiometer data are sufficient to predict yellow rust scores using Random Forest (RF) modelling. The SVIs were selected through RF-based recursive feature elimination (RFE), and the predicted scores in the resulting models had a prediction accuracy of r s = 0.50–0.61 when measuring the correlation between predicted and observed scores. Some of the most important spectral features for prediction were the Plant Senescence Reflectance Index (PSRI), Photochemical Reflectance Index (PRI), Red-Green Pigment Index (RGI), and Greenness Index (GI). Conclusions The proposed HTPP method of combining SVI data from spectral sensors in RF models, has the potential to be deployed in wheat breeding trials to score yellow rust. |
| first_indexed | 2024-12-13T10:38:42Z |
| format | Article |
| id | doaj.art-f1f8b287e558420ebd20f7807180b163 |
| institution | Directory Open Access Journal |
| issn | 1746-4811 |
| language | English |
| last_indexed | 2024-12-13T10:38:42Z |
| publishDate | 2022-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj.art-f1f8b287e558420ebd20f7807180b1632022-12-21T23:50:39ZengBMCPlant Methods1746-48112022-03-0118111110.1186/s13007-022-00868-0Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learningAlexander Koc0Firuz Odilbekov1Marwan Alamrani2Tina Henriksson3Aakash Chawade4Department of Plant Breeding, Swedish University of Agricultural SciencesDepartment of Plant Breeding, Swedish University of Agricultural SciencesDepartment of Plant Breeding, Swedish University of Agricultural SciencesLantmännen LantbrukDepartment of Plant Breeding, Swedish University of Agricultural SciencesAbstract Background High-throughput plant phenotyping (HTPP) methods have the potential to speed up the crop breeding process through the development of cost-effective, rapid and scalable phenotyping methods amenable to automation. Crop disease resistance breeding stands to benefit from successful implementation of HTPP methods, as bypassing the bottleneck posed by traditional visual phenotyping of disease, enables the screening of larger and more diverse populations for novel sources of resistance. The aim of this study was to use HTPP data obtained through proximal phenotyping to predict yellow rust scores in a large winter wheat field trial. Results The results show that 40–42 spectral vegetation indices (SVIs) derived from spectroradiometer data are sufficient to predict yellow rust scores using Random Forest (RF) modelling. The SVIs were selected through RF-based recursive feature elimination (RFE), and the predicted scores in the resulting models had a prediction accuracy of r s = 0.50–0.61 when measuring the correlation between predicted and observed scores. Some of the most important spectral features for prediction were the Plant Senescence Reflectance Index (PSRI), Photochemical Reflectance Index (PRI), Red-Green Pigment Index (RGI), and Greenness Index (GI). Conclusions The proposed HTPP method of combining SVI data from spectral sensors in RF models, has the potential to be deployed in wheat breeding trials to score yellow rust.https://doi.org/10.1186/s13007-022-00868-0High-throughput phenotypingPlant breedingYellow rustField phenotypingSpectral vegetation indexLow-cost phenotyping |
| spellingShingle | Alexander Koc Firuz Odilbekov Marwan Alamrani Tina Henriksson Aakash Chawade Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning Plant Methods High-throughput phenotyping Plant breeding Yellow rust Field phenotyping Spectral vegetation index Low-cost phenotyping |
| title | Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning |
| title_full | Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning |
| title_fullStr | Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning |
| title_full_unstemmed | Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning |
| title_short | Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning |
| title_sort | predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning |
| topic | High-throughput phenotyping Plant breeding Yellow rust Field phenotyping Spectral vegetation index Low-cost phenotyping |
| url | https://doi.org/10.1186/s13007-022-00868-0 |
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