%0 Conference Proceedings
%4 sid.inpe.br/marte2/2017/10.23.19.36.57
%2 sid.inpe.br/marte2/2017/10.23.19.36.58
%@isbn 978-85-17-00088-1
%F 59571
%T Interpretação de imagens de drone e do sensor OLI/ Landsat 8 para identificação de pragas e doenças na cana-de-açúcar
%D 2017
%A Alves, Matheus Oliveira,
%A Ferreira, Ricardo Vicente,
%A Custodio, Veruska Bichuette,
%@electronicmailaddress matheus_cefetiano@hotmail.com
%E Gherardi, Douglas Francisco Marcolino,
%E Aragão, Luiz Eduardo Oliveira e Cruz de,
%B Simpósio Brasileiro de Sensoriamento Remoto, 18 (SBSR)
%C Santos
%8 28-31 maio 2017
%I Instituto Nacional de Pesquisas Espaciais (INPE)
%J São José dos Campos
%P 1432-1438
%S Anais
%1 Instituto Nacional de Pesquisas Espaciais (INPE)
%X The precision agriculture has an important contribution to the efficient and assertive management of issues in the agriculture production, such as the occurrence of pests and failures in the planting. For this, the practice makes use of technological means such as remote sensing for recognition and characterization of the properties (conformities and non-conformities) that the area cultivated shows meter by meter. Based on the existing literature of the subject, the Normalize Difference Vegetation Index (NDVI) and the combined use of two remote sensing devices low-cost: the images provided by OLI/ Landsat 8 sensor and images captured by the drone Phantom 3 Stardard aiming to map and identify the occurrence of pests, diseases or nutritional deficiencies in sugar cane crops in 3 farms in municipality of Conceição das Alagoas, Brazil Minas Gerais, taken as cases of study. The work consists in the utilization of NDVI to identify areas with low productivity in sugarcane by visual interpretation and observation of pixels values. Then, a check field in this areas is assisted by interpretation of drone images shoted at a height of 50 meters above the ground. The satellite images were processed in the software ArcGIS 10.0 and the information led local field teams to verify and confirm the abnormalities identified remotely by this work showing the efficacy of the low cost remote sensing. As conclusion, the work indicate that use of low-cost technology of remote sensing and the proposed methodology contributes to map large areas and optimize futures manual sampling works.
%9 Monitoramento agrícola
%@language pt
%3 59571.pdf