Koc, Alexander and Odilbekov, Firuz and Alamrani, Marwan and Henriksson, Tina and Chawade, Aakash
(2022).
Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning.
Plant Methods. 18
:1
, 30
[Research article]
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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.
Authors/Creators: | Koc, Alexander and Odilbekov, Firuz and Alamrani, Marwan and Henriksson, Tina and Chawade, Aakash | ||||||
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Title: | Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning | ||||||
Series Name/Journal: | Plant Methods | ||||||
Year of publishing : | 2022 | ||||||
Volume: | 18 | ||||||
Number: | 1 | ||||||
Article number: | 30 | ||||||
Number of Pages: | 11 | ||||||
Publisher: | BMC | ||||||
ISSN: | 1746-4811 | ||||||
Language: | English | ||||||
Publication Type: | Research article | ||||||
Article category: | Scientific peer reviewed | ||||||
Version: | Published version | ||||||
Copyright: | Creative Commons: Attribution 4.0 | ||||||
Full Text Status: | Public | ||||||
Subjects: | (A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Agricultural Science | ||||||
Keywords: | High-throughput phenotyping, Plant breeding, Yellow rust, Field phenotyping, Spectral vegetation index, Low-cost phenotyping, Winter wheat, Disease resistance | ||||||
URN:NBN: | urn:nbn:se:slu:epsilon-p-116572 | ||||||
Permanent URL: | http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-116572 | ||||||
Additional ID: |
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ID Code: | 27502 | ||||||
Faculty: | LTV - Fakulteten för landskapsarkitektur, trädgårds- och växtproduktionsvetenskap | ||||||
Department: | (LTJ, LTV) > Department of Plant Breeding (from 130101) | ||||||
Deposited By: | SLUpub Connector | ||||||
Deposited On: | 01 Apr 2022 13:25 | ||||||
Metadata Last Modified: | 01 Apr 2022 13:31 |
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