Simple item page
 

Artificial neural network (ANN)-based algorithms for high light stress phenotyping of tomato genotypes using chlorophyll fluorescence features

cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.author-orcid0000-0002-0953-7045
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcid0af80967-45b1-40e8-a0bf-9989e4e639c2
dc.abstract.enHigh light (HL) is a common environmental stress directly imposes photoinhibition on the photosynthesis apparatus. Breeding plants for tolerance against HL is therefore highly demanded. Chlorophyll fluorescence (ChlF) is a sensitive indicator of stress in plants and can be evaluated using OJIP transients. In this study, we compared the ChlF features of plants exposed to HL (1200 μmol m−2 s−1) with that of control plants (300 μmol m−2 s−1). To extract the most reliable ChlF features for discrimination between HL-stressed and non-stressed plants, we applied three artificial neural network (ANN)-based algorithms, namely, Boruta, Support Vector Machine (SVM), and Recursive Feature Elimination (RFE). Feature selection algorithms identified multiple features but only two features, namely the maximal quantum yield of PSII photochemistry (FV/FM) and quantum yield of energy dissipation (ɸD0), remained consistent across all genotypes in control conditions, while exhibited variation in HL. Therefore, considered reliable features for HL stress screening. The selected features were then used for screening 14 tomato genotypes for HL. Genotypes were categorized into three groups, tolerant, semi-tolerant, and sensitive genotypes. Foliar hydrogen peroxide (H2O2) and malondialdehyde (MDA) contents were measured as independent proxies for benchmarking selected features. Tolerant genotypes were attributed with the lowest change in H2O2 and MDA contents, while the sensitive genotypes displayed the highest magnitude of increase in H2O2 and MDA by HL treatment compared to the control. Finally, a FV/FM higher than 0.77 and ɸD0 lower than 0.24 indicates a healthy electron transfer chain (ETC) when tomato plants are exposed to HL.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Ekologii i Ochrony Åšrodowiska
dc.contributor.authorShomali, Aida
dc.contributor.authorAliniaeifard, Sasan
dc.contributor.authorBakhtiarizadeh, Mohammad Reza
dc.contributor.authorLotfi, Mahmoud
dc.contributor.authorMohammadian, Mohammad
dc.contributor.authorVafaei Sadi, Mohammad Sadegh
dc.contributor.authorRastogi, Anshu
dc.date.accessioned2025-10-29T09:11:22Z
dc.date.available2025-10-29T09:11:22Z
dc.date.issued2023
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if6,1
dc.description.numberAugust 2023
dc.description.points70
dc.description.volume201
dc.identifier.doi10.1016/j.plaphy.2023.107893
dc.identifier.eissn1873-2690
dc.identifier.issn0981-9428
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/5586
dc.languageen
dc.relation.ispartofPlant Physiology and Biochemistry
dc.relation.pagesart. 107893
dc.rightsClosedAccess
dc.sciencecloudnosend
dc.subject.enartificial neural network
dc.subject.enbruta
dc.subject.enfeature selection
dc.subject.enlight stress
dc.subject.enOJIP
dc.subject.enphotosynthesis
dc.subject.enRFE
dc.subject.enSVM
dc.titleArtificial neural network (ANN)-based algorithms for high light stress phenotyping of tomato genotypes using chlorophyll fluorescence features
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.volume201