Generalized Linear Model with Elastic Net Regularization and Convolutional Neural Network for Evaluating Aphanomyces Root Rot Severity in Lentil.
Journal
Plant phenomics (Washington, D.C.)
ISSN: 2643-6515
Titre abrégé: Plant Phenomics
Pays: United States
ID NLM: 101769942
Informations de publication
Date de publication:
2020
2020
Historique:
received:
30
11
2019
accepted:
21
09
2020
entrez:
12
2
2021
pubmed:
13
2
2021
medline:
13
2
2021
Statut:
epublish
Résumé
Phenomics technologies allow quantitative assessment of phenotypes across a larger number of plant genotypes compared to traditional phenotyping approaches. The utilization of such technologies has enabled the generation of multidimensional plant traits creating big datasets. However, to harness the power of phenomics technologies, more sophisticated data analysis methods are required. In this study, Aphanomyces root rot (ARR) resistance in 547 lentil accessions and lines was evaluated using Red-Green-Blue (RGB) images of roots. We created a dataset of 6,460 root images that were annotated by a plant breeder based on the disease severity. Two approaches, generalized linear model with elastic net regularization (EN) and convolutional neural network (CNN), were developed to classify disease resistance categories into three classes: resistant, partially resistant, and susceptible. The results indicated that the selected image features using EN models were able to classify three disease categories with an accuracy of up to 0.91 ± 0.004 (0.96 ± 0.005 resistant, 0.82 ± 0.009 partially resistant, and 0.92 ± 0.007 susceptible) compared to CNN with an accuracy of about 0.84 ± 0.009 (0.96 ± 0.008 resistant, 0.68 ± 0.026 partially resistant, and 0.83 ± 0.015 susceptible). The resistant class was accurately detected using both classification methods. However, partially resistant class was challenging to detect as the features (data) of the partially resistant class often overlapped with those of resistant and susceptible classes. Collectively, the findings provided insights on the use of phenomics techniques and machine learning approaches to provide quantitative measures of ARR resistance in lentil.
Identifiants
pubmed: 33575665
doi: 10.34133/2020/2393062
pmc: PMC7870103
doi:
Types de publication
Journal Article
Langues
eng
Pagination
2393062Informations de copyright
Copyright © 2020 Afef Marzougui et al.
Déclaration de conflit d'intérêts
The authors declare that there are no conflicts of interest regarding the publication of this article.
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