Detecção automática e avaliação de linhas de plantio de cana-de-açúcar em imagens aéreas

• Ano de defesa: 2021
• Autor(a) principal: Rocha, Bruno Moraes
• Orientador(a): Soares, Fabrízzio Alphonsus Alves de Melo Nunes
• Banca de defesa: Soares, Fabrízzio Alphonsus Alves de Melo Nunes, Pedrini, Hélio, Salvini, Rogerio Lopes, Costa, Ronaldo Martins da, Cabacinha, Christian Dias
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Ciência da Computação (INF)
• Departamento: Instituto de Informática - INF (RG)
• País: Brasil
• Palavras-chave em Português: Gradiente RGB; Plantação de cana-de-açúcar; Aeronave pilotada remotamente; Ortomosaico
• Palavras-chave em Inglês: RGB gradient; Planting rows; Remotely piloted aircraft; Orthomosaic
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/11840

For higher productivity and economic yield in sugarcane field, several imaging techniques using sugarcane field images have been developed. However, the identification and measurement of gaps in sugarcane field crop rows are still commonly performed manually on site to decide to replant the gaps or the entire area. Manual measurement has a high cost of time and manpower. Based on these factors, this study aimed to create a new technique that automatically identifies and evaluates the gaps along the crop rows in aerial images of sugarcane fields obtained by a small remotely piloted aircraft. The images captured using the remotely piloted aircraft were used to generate the orthomosaics of the crop field area and classified with the algorithm K-Nearest Neighbors to segment the crop rows. The orientation of the planting rows in the image was found using the filter gradient Red Green Blue. Then, the crop rows were mapped using the curve adjustment method and overlap the classified image to detect and measure the gaps along the segment of the planting line. The technique developed obtained a maximum error of approximately 3% when compared to the manual method to evaluate the length of the gaps in the crop rows in an orthomosaic with an area of 8.05 hectares using the method proposed by Stolf, adapted for digital images. The proposed approach was able to properly identify the spatial position of automatically generated line segments over manually created line segments. The proposed method was also able to achieve statistically similar results when confronted with the technique performed manually in the image for the mapping of rows and identification of gaps for sugarcane fields with growth 40 and 80 days after planting. The automatic technique developed had a significant result in the evaluation of the gaps in the crop rows in the aerial images of sugarcane fields, thus, its use allows automated inspections with high accuracy measurements, and besides being able to assist producers in making decisions in the management of their sugarcane fields.