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Generalized Linear Model with Elastic Net Regularization and Convolutional Neural Network for Evaluating Aphanomyces Root Rot Severity in Lentil

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Plant Phenomics 2020年第1期2卷 378-388页

作者： afef marzougui Yu Ma Rebecca J.McGee Lav R.Khot Sindhuja Sankaran Department of Biological Systems Engineering Washington State UniversityPullmanWAUSA Department of Horticulture Washington State UniversityPullmanWAUSA United States Department of Agriculture-Agricultural Research Service Grain Legume Genetics and Physiology Research UnitWashington State UniversityPullmanWAUSA

Phenomics technologies allow quantitative assessment of phenotypes across a larger number of plant genotypes compared to traditional phenotyping *** utilization of such technologies has enabled the generation of multidimensional plant traits creating big ***,to harness the power of phenomics technologies,more sophisticated data analysis methods are *** this study,Aphanomyces root rot(ARR)resistance in 547 lentil accessions and lines was evaluated using Red-Green-Blue(RGB)images of *** created a dataset of 6,460 root images that were annotated by a plant breeder based on the disease *** 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 *** 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 ***,partially resistant class was challenging to detect as the features(data)of the partially resistant class often overlapped with those of resistant and susceptible ***,the findings provided insights on the use of phenomics techniques and machine learning approaches to provide quantitative measures of ARR resistance in lentil.

关键词： classify Elastic roots

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Evaluation of biogenic markers-based phenotyping for resistance to Aphanomyces root rot in field pea

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Information Processing in Agriculture 2022年第1期9卷 1-10页

作者： afef marzougui Abirami Rajendran D.Scott Mattinson Yu Ma Rebecca J.McGee Manuel Garcia-Perez Stephen P.Ficklin Sindhuja Sankaran Department of Biological Systems Engineering Washington State UniversityPullmanWAUnited States Department of Horticulture Washington State UniversityPullmanWAUnited States United States Department of Agriculture-Agricultural Research Service Grain Legume Genetics and Physiology Research UnitWashington State UniversityPullmanWAUnited States

Biogenic volatile organic compounds(VOCs)emitted by plants can reveal information about plant adaptation,defense processes,and biological ***,such VOC data may be utilized to capture phenotypic plant responses to the *** this study,the main objective was to evaluate the potential of biogenic compounds,including VOCs,to phenotype two pea cultivars,Ariel(susceptible)and Hampton(high levels of partial resistance)for resistance to Aphanomyces root rot *** were monitored non-destructively for VOC emission at three-time points(15,20,and 30 days after inoculation,DAI)using dynamic headspace sampling with gas chromatography-flame ionization detec-tion(GC-FID)system,as well as destructively at the end of the experiments,using solvent extraction and pyrolysis of both shoot and root tissues.A non-inoculated control(mock-inoculated with distilled water)was utilized to compare the plant responses within a *** common chemical peaks between control and inoculated samples of both culti-vars(RT_(cm))were analyzed after normalizing the relative peak intensity of inoculated samples with those of control samples,prior to a comparison between *** addi-tion,unique chemical peaks(RT_(uq))present in inoculated samples,but not in control sam-ples were also identified and their relative peak intensities were *** the released green leaf volatiles(RT_(cm)),the normalized relative peak intensity of hexanal emis-sion,at 20 DAI,was higher in Ariel than that of *** addition,several putative chemical peaks(both RT_(cm) and RT_(uq)),previously known as indicators for disease response,exhibited some differences in their emission rates between pea cultivars in at least one of the time *** destructive sampling revealed that shoot samples produced more putative unique biomarkers(RT_(uq))than the root *** on the differences in puta-tive chemical peaks between cultivars,this initial study supports the concept of utilizat

关键词： Plant breeding Pulse crop Volatile organic compounds Plant phenotyping Biotic stress

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