Title:	Temporal field-based phenomics for evaluating transgenic maize under drought stress.
Authors:	YASSITEPE, J. E. de C. T. PEREIRA, H. D. NONATO, J. V. A. MOLTOCARO, R. C. R. GERHARDT, I. R. DANTE, R. A. ARRUDA, P.
Affiliation:	JULIANA ERIKA DE CARVALHO TEIXEIRA YASSITEPE, CNPTIA; HELCIO D. PEREIRA, GENOMICS FOR CLIMATE CHANGE RESEARCH CENTER, CAMPINAS, SP, BRAZIL; CENTRO DE BIOLOGIA MOLECULAR E ENGENHARIA GENÉTICA, UNICAMP, CAMPINAS, SP, BRAZIL; JULIANNA V. A. NONATO, GENOMICS FOR CLIMATE CHANGE RESEARCH CENTER, CAMPINAS, SP, BRAZIL; CENTRO DE BIOLOGIA MOLECULAR E ENGENHARIA GENÉTICA, UNICAMP, CAMPINAS, SP, BRAZIL; RAFAELA CAROLINE RANGNI MOLTOCARO DUARTE, CNPMA; ISABEL RODRIGUES GERHARDT, CNPTIA; RICARDO AUGUSTO DANTE, CNPTIA; PAULO ARRUDA, GENOMICS FOR CLIMATE CHANGE RESEARCH CENTER, CAMPINAS, SP, BRAZIL.
Date Issued:	2025
Citation:	In: ANNUAL MAIZE GENETICS MEETING, 67., 2025, St. Louis. Program and abstracts. Beltsville: USDA, 2025. P269.
Pages:	p. 228.
Description:	Climate change has increased the need for drought-resilient crops, yet traditional assessment methods are labor-intensive. This study utilized an unmanned aerial system (UAS) with RGB and multispectral sensors to monitor transgenic maize hybrids under irrigated and drought conditions. Machine learning models revealed strong correlations between vegetation indices and phenotypic traits, with RGB sensors outperforming multispectral sensors in trait prediction. Prediction accuracies ranged from 0.35 to 0.70 for traits like grain yield, days to anthesis, and plant height. Ridge regression and random forest models provided the best predictions. The vegetation indices NGRDI, VARI, and RCC effectively predicted and captured the plant response to drought. This study demonstrates the potential of UAS phenotyping as an efficient tool for assessing drought resilience in maize breeding programs.
Thesagro:	Milho Seca Resistência a Seca
NAL Thesaurus:	Transgenic plants
Type of Material:	Resumo em anais e proceedings
Access:	openAccess
Appears in Collections:	Resumo em anais de congresso (CNPMA)