Título:	Creation of a gala apple fruit image database for Brazil.
Autor:	SESSI, A. S. FRANCO, A. F. M. F. SILVA, E. C. da MARCHIORETTO, L. de R. DIAS, J. M. ALVES, S. A. M. GEBLER, L.
Afiliación:	ALESSANDRA SOARES SESSI, EMBRAPA UVA E VINHO; ALESSANDRO FERNANDO MACHADO FRANCO, EMBRAPA UVA E VINHO; EDUARDO CARVALHO DA SILVA, EMBRAPA UVA E VINHO; LUCAS DE ROSS MARCHIORETTO, EMBRAPA UVA E VINHO; JOVANIA MENEZES DIAS, INSTITUTO FEDERAL DO RIO GRANDE DO SUL; SILVIO ANDRE MEIRELLES ALVES, CNPUV; LUCIANO GEBLER, CNPUV.
Año:	2025
Referencia:	In: WORKSHOP CIENTÍFICO DO CENTRO DE CIÊNCIA PARA O DESENVOLVIMENTO EM AGRICULTURA DIGITAL – SEMEAR DIGITAL, 2., 2025, Campinas. Anais [...]. Piracicaba: ESALQ/USP, 2025. p. 19-26.
Páginas:	7 p.
Descripción:	Information on land use and coverage is necessary to assist in the management process and assertive decision-making. Thus, the present study aimed to evaluate the fusion of Sentinel-1 (S1) and Sentinel-2 (S2) data in the mapping of land use and coverage of the municipality of Lagoinha (SP) using the Random Forest method. Three scenarios were tested for classification: data from (S1), (S2) and fusion of (S2+S1). To evaluate the accuracy of the classification, high-resolution images from Google Earth and S2 software were used. The overall accuracy of the classification from the combination of S2+S1 data was 94%, and the Kappa index was equal to 0.9. For the isolated images of S2 and S1, overall accuracies of 80% and 50% and Kappas index of 0.71 and 0.50 were obtained, respectively. The fusion of S1+S2 data showed high accuracy in mapping;
Thesagro:	Radar
NAL Thesaurus:	Remote sensing Land use
Palabras clave:	Sensor Fusion Machine Learning
Tipo de Material:	Artigo em anais e proceedings
Acceso:	openAccess
Aparece en las colecciones:	Artigo em anais de congresso (CNPUV)