Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1171965
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dc.contributor.authorPEREIRA, H. D.
dc.contributor.authorNONATO, J. V. A.
dc.contributor.authorMOLTOCARO, R. C. R.
dc.contributor.authorGERHARDT, I. R.
dc.contributor.authorDANTE, R. A.
dc.contributor.authorARRUDA, P.
dc.contributor.authorYASSITEPE, J. E. de C. T.
dc.date.accessioned2025-01-24T23:16:05Z-
dc.date.available2025-01-24T23:16:05Z-
dc.date.created2025-01-24
dc.date.issued2025
dc.identifier.citationThe Plant Phenome Journal, v. 8, n. 1, e70015, Dec. 2025.
dc.identifier.issn2578-2703
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1171965-
dc.descriptionGlobal climate change has driven breeding programs to develop abiotic stress‐resilient plant varieties. Traditionally, assessing drought resilience involves labor‐intensive and time‐consuming processes. This study used an unmanned aerial system (UAS) to predict key phenotyping traits in maize (Zea mays L.) and monitor plant response to drought during the crop cycle. We grew transgenic maize hybrids in two trials, one irrigated and another subjected to drought stress, and used a drone equipped with red–green–blue (RGB) and multispectral sensors to capture images of the plots over time. Machine learning models and various prediction scenarios revealed significant correlations between vegetation indices over time. Interestingly, the RGB sensor outperformed the multispectral sensor in trait prediction. Prediction accuracy across scenarios with untested genotypes and environments ranged from 0.40 to 0.70 for grain yield, 0.43 to 0.69 for days to anthesis, 0.51 to 0.67 for days to silking, and 0.35 to 0.57 for plant height. Ridge and random forest models consistently delivered the most accurate predictions across traits and environments. The vegetation indices normalized green–red difference index, VARI, and RCC also effectively predicted and captured the plant response to drought. This study highlights the value of UAS phenotyping as a practical tool for assessing abiotic stress due to its straightforward implementation.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectResposta à seca
dc.subjectAprendizado de máquina
dc.subjectDrought stress
dc.subjectMachine learning
dc.titleTemporal field phenomics of transgenic maize events subjected to drought stress: cross‐validation scenarios and machine learning models.
dc.typeArtigo de periódico
dc.subject.thesagroMilho
dc.subject.thesagroZea Mays
dc.subject.nalthesaurusDrought tolerance
riaa.ainfo.id1171965
riaa.ainfo.lastupdate2025-01-24
dc.identifier.doihttps://doi.org/10.1002/ppj2.70015
dc.contributor.institutionHELCIO DUARTE PEREIRA, UNIVERSIDADE ESTADUAL DE CAMPINAS; JULIANA VIEIRA ALMEIDA NONATO, UNIVERSIDADE ESTADUAL DE CAMPINAS; RAFAELA C RANGNI MOLTOCARO DUARTE, CNPMA; ISABEL RODRIGUES GERHARDT, CNPTIA, UNIVERSIDADE ESTADUAL DE CAMPINAS; RICARDO AUGUSTO DANTE, CNPTIA, UNIVERSIDADE ESTADUAL DE CAMPINAS; PAULO ARRUDA, UNIVERSIDADE ESTADUAL DE CAMPINAS; JULIANA ERIKA DE CARVALHO TEIXEIRA, CNPTIA, UNIVERSIDADE ESTADUAL DE CAMPINAS.
Aparece nas coleções:Artigo em periódico indexado (CNPMA)

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