Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/49783
DC FieldValueLanguage
dc.contributor.authorAntonucci, Francescait
dc.contributor.authorCosta, Corradoit
dc.contributor.authorFigorilli, Simoneit
dc.contributor.authorOrtenzi, Lucianoit
dc.contributor.authorManganiello, Rossellait
dc.contributor.authorSantangelo, Enricoit
dc.contributor.authorGierz, Łukaszit
dc.contributor.authorPallottino, Federicoit
dc.date.accessioned2023-05-24T14:32:18Z-
dc.date.available2023-05-24T14:32:18Z-
dc.date.issued2023it
dc.identifier.issn2076-3417it
dc.identifier.urihttp://hdl.handle.net/2067/49783-
dc.description.abstractThe development of high-throughput field phenotyping, which uses modern detection technologies and advanced data processing algorithms, could increase productivity and make in-field phenotypic evaluation more efficient by collecting large amounts of data with no or minimal human assistance. Moreover, high-throughput plant phenotyping systems are also very effective in selecting crops and characterizing germplasm for drought tolerance and disease resistance by using spectral sensor data in combination with machine learning. In this study, an affordable high-throughput phenotyping platform (phenomobile) aims to obtain solutions at reasonable prices for all the components that make up it and the many data collected. The goal of the practical innovation in field phenotyping is to implement high-performance precision phenotyping under real-world conditions at accessible costs, making real-time data analysis techniques more user-friendly. This work aims to test the ability of a phenotyping prototype system constituted by an electric phenomobile integrated with a MAIA multispectral camera for real in-field plant characterization. This was done by acquiring spectral signatures of F1 hybrid Elisir (Olter Sementi) tomato plants and calculating their vegetation indexes. This work allowed to collect, in real time, a great number of field data about, for example, the morphological traits of crops, plant physiological activities, plant diseases, fruit maturity, and plant water stress.it
dc.format.mediumELETTRONICOit
dc.language.isoitait
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleA Low-Cost Sensorized Vehicle for In-Field Crop Phenotypingit
dc.typearticle*
dc.identifier.doi10.3390/app13042436it
dc.identifier.scopus2-s2.0-85149793617it
dc.identifier.urlhttps://www.mdpi.com/2076-3417/13/4/2436it
dc.relation.journalAPPLIED SCIENCESit
dc.relation.article2436it
dc.relation.volume13it
dc.relation.issue4it
dc.subject.scientificsectorINF/01 INFORMATICAit
dc.subject.keywordsreal-time analysis; phenomobile; multispectral camera; sensors; proximal sensingit
dc.subject.ercsectorPE6_3, PE6_6it
dc.description.numberofauthors8it
dc.description.internationalit
dc.description.noteAuthor Contributions Conceptualization, C.C., S.F. and F.P.; methodology, C.C., S.F., L.O., E.S. and F.P.; software, S.F. and L.O.; validation, F.A., C.C., S.F. and F.P; formal analysis, C.C., S.F., E.S. and L.O.; investigation, F.A, C.C., S.F. and F.P; resources, C.C.; data curation, C.C., F.P., R.M. and F.A.; writing—original draft preparation, R.M. and F.A.; writing—review and editing, R.M., Ł.G. and F.A.; visualization, C.C, F.A. and F.P.; supervision, C.C. and F.P.; project administration, C.C. funding acquisition, C.C and F.P. All authors have read and agreed to the published version of the manuscript.it
dc.contributor.countryITAit
dc.contributor.countryPOLit
dc.type.refereeREF_1it
dc.type.miur262*
item.fulltextWith Fulltext-
item.openairetypearticle-
item.cerifentitytypePublications-
item.grantfulltextrestricted-
item.languageiso639-1it-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.orcid0000-0002-1245-8882-
crisitem.journal.journalissn2076-3417-
crisitem.journal.anceE217683-
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