The Time of Day Is Key to Discriminate Cultivars of Sugarcane upon Imagery Data from Unmanned Aerial Vehicle

• Autor(a) principal: Barbosa Júnior, Marcelo Rodrigues [UNESP]
• Data de Publicação: 2022
• Outros Autores: Tedesco, Danilo [UNESP], Carreira, Vinicius Dos Santos [UNESP], Pinto, Antonio Alves [UNESP], Moreira, Bruno Rafael de Almeida [UNESP], Shiratsuchi, Luciano Shozo, Zerbato, Cristiano [UNESP], da Silva, Rouverson Pereira [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.3390/drones6050112; http://hdl.handle.net/11449/241832
• topic: flight time; NDVI; principal component analysis; reflectance; remote sensing; Saccharum spp; spectral band; UAV; vegetation index

Remote sensing can provide useful imagery data to monitor sugarcane in the field, whether for precision management or high-throughput phenotyping (HTP). However, research and tech-nological development into aerial remote sensing for distinguishing cultivars is still at an early stage of development, driving the need for further in-depth investigation. The primary objective of this study was therefore to analyze whether it could be possible to discriminate market-grade cultivars of sugarcane upon imagery data from an unmanned aerial vehicle (UAV). A secondary objective was to analyze whether the time of day could impact the expressiveness of spectral bands and vegetation indices (VIs) in the biophysical modeling. The remote sensing platform acquired high-resolution imagery data, making it possible for discriminating cultivars upon spectral bands and VIs without computational unfeasibility. 12:00 PM especially proved to be the most reliable time of day to perform the flight on the field and model the cultivars upon spectral bands. In contrast, the discrimination upon VIs was not specific to the time of flight. Therefore, this study can provide further information about the division of cultivars of sugarcane merely as a result of processing UAV imagery data. Insights will drive the knowledge necessary to effectively advance the field’s prominence in developing low-altitude, remotely sensing sugarcane.