Data/hora: 25/06/2024 19:01:58

Biblioteca(s): | Embrapa Algodão.
Data corrente: | 22/08/2023
Data da última atualização: | 23/08/2023
Autoria: | CARNEIRO, F. M.; BRITO FILHO, A. L. de; MARTINS, M. de S.; BRANDÃO, Z. N.; SHIRATSUCHI, L. S.
Afiliação: | FRANCIELE MORLIN CARNEIRO, UTFPR; ARMANDO OPES DE BRITO FILHO, UNESP; MURILO DE SANTANA MARTINS, LOUISIANA STATE UNIVERSITY; ZIANY NEIVA BRANDÃO, CNPA; LUCIANO SHOZO SHIRATSUCHI, LOUISIANA STATE UNIVERSITY.
Título: | Machine learning approach for corn nitrogen recommendation.
Ano de publicação: | 2023
Fonte/Imprenta: | In: SILVA-MATOS, R. R. S. da; DOIHARA, I. P.; LINHARES, S. C. (org.). Medio ambiente: Agricultura, desarrollo y sustentabilidad 2. Ponta Grossa, PR: Atena, 2023. c. 3.
Páginas: | p. 21-28.
ISBN: | 978-65-258-1530-5
DOI: | https://doi.org/10.22533/at.ed.3052302083
Idioma: | Inglês
Conteúdo: | Nitrogen (N) fertilizer recommendation tools are vital to precise agricultural management. The objectives of this research were to determine how many variables and remote sensor dataare needed to prescribe N fertilizer in corn (Zea mays L.), PFP (partial factor productivity), and yield integrating remote sensing and soil sensor technologies. The variables of this work were NIR, Red, Red-Edge wavelengths, plant height, canopy temperature, LAI (leaf area index), and apparent soil electrical conductivity (ECa). Random Forest Classifier was used to select the best input to estimate N rates, PFP, and corn yield. A confusion matrix was used to identify the accuracy of the Random Forest Classifier to detect the best inputs to estimate for which input we evaluated in this work. According to Random Forest, the best inputs to estimate the N rate and PFP were Red-Edge, Red, and NIR wavelengths, plant height, and canopy temperature. For estimate corn yield were: NIR wavelengths, N rates, plant height, Red-Edge, and canopy temperature.
Palavras-Chave: | Active sensor; Estimativa de produtividade; Linguagem de máquina; Machine learning; Maize; Random Forest; Sensor ativo; Yield estimate.
Thesagro: | Desenvolvimento Sustentável; Meio Ambiente; Milho; Sensoriamento Remoto.
Thesaurus Nal: | Environment; Remote sensing; Sustainable development.
Categoria do assunto: | P Recursos Naturais, Ciências Ambientais e da Terra
Registro original: | Embrapa Algodão (CNPA)
Exemplares: | CNPA - (29084-1) - ADD - PL - DD -  -  -  - DIS