Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/50118
Título: Uso de aeronaves remotamente pilotadas na detecção do bicho mineiro nos cafeeiros
Título(s) alternativo(s): Use of remotely pilotted aircraft in the detection of the bicho mineiro in the coffee trees
Autores: Silva, Fabio Moreira da
Silva, Fabio Moreira da
Alves, Marcelo de Carvalho
Páscoa, Kalill José Viana da
Palavras-chave: Agricultura de precisão
Sensoriamento remoto
Bicho-mineiro-do-cafeeiro
Geoprocessamento
Leucoptera coffeella
Auto ML Forester
Precision agriculture
Remote sensing
Coffee leaf miner
Geoprocessing
Data do documento: 7-Jun-2022
Editor: Universidade Federal de Lavras
Citação: SOUZA, N. P. de. Uso de aeronaves remotamente pilotadas na detecção do bicho mineiro nos cafeeiros. 2022. 64 p. Dissertação (Mestrado em Engenharia Agrícola) – Universidade Federal de Lavras, Lavras, 2022.
Resumo: Remote sensing (SR) is a technique whose fundamental principle is to obtain data from an object without necessarily having direct contact. short time and cover large areas compared to conventional field methods. Therefore, detecting pests such as the coffee leaf miner (Leucoptera coffeella) becomes a great challenge, since it is a phytosanitary problem of coffee plants and is important for Brazilian agribusiness. In this context, the objective is monitoring for its detection, using a remotely piloted aircraft (RPAs) with RGB cameras and obtaining high resolution images combined with machine learning algorithms. The experiment was carried out on the farm called Barro preto, located in the city of Lavras, state of Minas Gerais, Brazil. The results achieved were the generation of 4 models derived from machine learning, being the ‘ranger’ model derived from Random Forest with 90% accuracy in detecting the presence of leaf miner infestation through RGB image, with sensitivity adjustment 88% of detecting the object in the field and specificity of 91% demonstrating that there is a real possibility of managing and managing the coffee crop, through low-cost RGB images, in relation to the miner infestation.
URI: http://repositorio.ufla.br/jspui/handle/1/50118
Aparece nas coleções:Engenharia Agrícola - Mestrado (Dissertações)

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