Título:	Models for predicting biometric variables in cowpea using multispectral aerial images.
Autoria:	ANDRADE JUNIOR, A. S. de SOBRAL, A. H. S. BASTOS, E. A. PESSOA FILHO, F. N. NUNES, L. P. ROIG, H.
Afiliação:	ADERSON SOARES DE ANDRADE JUNIOR, CPAMN AMANDA HELLEN SALES SOBRAL, FEDERAL UNIVERSITY OF PIAUI EDSON ALVES BASTOS, CPAMN FRANCINALDO NUNES PESSOA FILHO, STATE UNIVERSITY OF PIAUI LEANDRO PESSOA NUNES, STATE UNIVERSITY OF PIAUI HENRIQUE ROIG, INSTITUTO DE GEOCIÊNCIAS, UNB, BRASÍLIA, DF.
Ano de publicação:	2023
Referência:	Teresina: Embrapa Meio-Norte, 2023.
Páginas:	54 p.
Conteúdo:	Models based on vegetation indices (VI) from digital aerial images are promising for predicting biometric variables in agricultural crops. The objective of this study was to generate prediction models for leaf area index (LAI) and shoot dry weight (SDW) of cowpea crops (cultivar BRS-Inhuma) based on VI derived from aerial images captured by a multispectral camera attached to a drone. The study was conducted at the experimental station of the Brazilian Agricultural Research Corporation (Embrapa Mid-North), in Teresina, PI, Brazil (5°05’S, 42°29’W, and altitude of 72 m) from September to October 2022. LAI was measured in the field and in laboratory, while SDW was measured in eight samples at 13, 19, 26, 33, 40, 47, 51, and 61 days after sowing.
Thesagro:	Agricultura de Precisão Sensoriamento Remoto
NAL Thesaurus:	Precision agriculture Remote sensing
Palavras-chave:	Spatial variability Variabilidade espacial RPA
Série:	(Embrapa Meio-Norte. Boletim de Pesquisa e Desenvolvimento, 154).
Tipo do Material:	Folhetos
Acesso:	openAccess
Aparece nas coleções:	Boletim de Pesquisa e Desenvolvimento (CPAMN)