Crop Drone: uma plataforma para coleta, armazenamento e disponibilização de dados agrícolas

• Ano de defesa: 2016
• Autor(a) principal: Cerbaro, Vinicius Andrei
• Orientador(a): Pavan, Willingthon
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade de Passo Fundo
• Programa de Pós-Graduação: Programa de Pós-Graduação em Computação Aplicada
• Departamento: Instituto de Ciências Exatas e Geociências – ICEG
• País: BR
• Palavras-chave em Português: Sensoriamento remoto; Programas de computador; Agricultura
• Palavras-chave em Inglês: Remote sensing; Computer programs; Agriculture
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAO
• Link de acesso: http://10.0.217.128:8080/jspui/handle/tede/22

Over the years, technological advances have been favoring the emergence of new solutions for the Agricultural Remote Sensing (ARS). These solutions allow more accurate evaluation of the soil and vegetation conditions, improving the quality of information used for decision making. The Unmanned Aerial Vehicles (UAVs) are part of these new solutions and have been acquiring more space in the ARS, where they are used to collect images. Once processed, these images result in vegetation indices (VI), complementing the data collected by satellites, aircraft and ground sensors. However, the high cost of UAVs and cameras that are used to capture the images and the dependence on a high knowledge of the techniques and tools for image processing, end up hindering the implementation of these solutions in projects that have few financial resources. In this context, the creation of a low-cost platform based on UAVs and computational tools to collect and process images of agricultural areas, becomes very important to spread the technology and make them usable in different projects. Thus, a low-cost platform composed of a UAV and a common camera, was developed. The camera was modified to collect images that allow the extraction of the Normalized Difference Vegetation Index (NDVI), a well known index which is much used for having a strong correlation with the growth of plants. Were also developed two computational solutions to process and store the images collected and the weather and management data related to the crop areas. Thus, during the development of this project, three independent solutions were integrated, creating a low-cost platform to collect, process and provide data of agricultural areas