Imagens multiespectrais para discriminar fontes de adubo no cafeeiro

• Ano de defesa: 2022
• Autor(a) principal: Rezende, Camila Isabel Pereira
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Agricultura e Informações Geoespaciais
• Palavras-chave em Português: Coffea arabica L.; manejo de adubação; monitoramento remoto de baixo custo; discriminação de tratamentos; fertilization management; low-cost remote monitoring; treatment discrimination; CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::CIENCIA DO SOLO::FERTILIDADE DO SOLO E ADUBACAO; CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::FITOTECNIA::MANEJO E TRATOS CULTURAIS; CNPQ::CIENCIAS EXATAS E DA TERRA::GEOCIENCIAS::GEODESIA::FOTOGRAMETRIA; Agronomia; Café - Adubos e fertilizantes; Fitotecnia; Imagens multiespectrais
• Link de acesso: https://repositorio.ufu.br/handle/123456789/34752; http://doi.org/10.14393/ufu.di.2022.140

Coffee fertilization is key for the entire plant development and must be managed according to the phenological stage of the crop. The source and dose of the fertilizer to be used is an important choice, as it affects not only productivity but also the chemical and biological properties of the soil. Remote monitoring of the management of coffee crops is necessary as the demand in decision-making, where the aim is to rise production based on sustainable management is in a constant growth. In this work, we evaluated the potential of images obtained by low-cost sensors in the discrimination of sources of mineral and organomineral fertilizers in coffee. The experimental design was in randomized blocks, with five blocks and six treatments, as follows: (TI) - 100% of the organomineral treatment; (T2) - 70% of the organomineral treatment; (T3) - 50% of the organomineral treatment; (T4) - 100% of mineral fertilization; (T5) - 70% of mineral fertilization (T6) - standard treatment of the farm. After management, we used the Mapir 3 Survey3W camera coupled to an ARP drone – Phantom4 to take images of the experiment over a 12-month vegetative period. Combined with image taking, we collected agronomic parameters of coffee growth and productivity for two crops and concluded that different fertilization doses did not significantly affect the analyzed characteristics. Based on the supervised classification of multispectral images, it was possible to discriminate treatments with a higher degree of accuracy (86.66% accuracy) than when analyzing coffee growth parameters.