An Unmanned Aerial Vehicles Journey into the World of Sugarcane

• Ano de defesa: 2024
• Autor(a) principal: Barbosa Júnior, Marcelo Rodrigues
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Estadual Paulista (Unesp)
• Palavras-chave em Português: Unmanned Aerial Vehicles; Sugarcane; Remote Sensing; Digital Agriculture; Machine Learning
• Link de acesso: https://hdl.handle.net/11449/255529; http://lattes.cnpq.br/7949757920964231; https://orcid.org/0000-0002-7207-2156

In this PhD Dissertation we investigated the use of unmanned aerial vehicles (UAVs) in sugarcane production, emphasizing their increasing importance in agriculture and the strategic role of sugarcane. The research includes an integrative review of UAV applications from 2016 to 2021, analyzing monitoring and management strategies and assessing the contributions of various countries and institutions. Consequently, we present a new protocol to determine ideal UAV flight times to discriminate sugarcane cultivars during early stage of development using multispectral images and vegetation indices. Furthermore, we used machine learning (ML) algorithms combined with UAV imagery to predict sugar content in sugarcane, offering a superior, non-invasive, and scalable method compared to traditional techniques. This approach was also enhanced with the incorporation of a proximal active sensor, improving the prediction capabilities for bioethanol feedstocks and promoting sustainable land use by considering all plant components as valuable resources. The final chapter summarizes the progress of UAV technology applications in sugarcane, discusses the utility and transferability of the methods developed, and describes future research directions to further advance in this field. Finally, this PhD Dissertation offers significant insight into optimizing sugarcane production through the integration of innovative technologies.