Abstract
Tree crops, such as Arabica coffee (Coffea arabica L.), present enormous technical challenges in terms of pesticide application. The correct deposition and distribution of the active ingredient throughout the aerial part of these plants depends on knowledge of the canopy volume, but manually determining this volume is time consuming and imprecise. The objectives of this study were to develop a method to determine the vegetation volume of coffee crops from digital images captured by camera onboard unmanned aerial vehicles and to compare this approach with traditional vegetation volume estimation (tree row volume (TRV) method). Manual measurements of the canopy volume of four coffee cultivation areas were compared with data obtained using the method presented in this paper. It was concluded that the vegetation volume of coffee trees, a highly important variable in defining pesticide application techniques (in addition to other uses), could be determined in a practical and precise way by digitally processing the images captured by unmanned aerial vehicles. The method is fast and permits the assessment of large areas. Furthermore, estimates based on this method and the traditional TRV method were not significantly different.
Highlights
Agriculture is increasingly linked to information technology and automation, and in this context, the unmanned aerial vehicle (UAV) is a tool with many applications (Gómez-Candón et al, 2014)
The coffee canopy volume was estimated via two methods, manually and using images collected by UAV, in four coffee plantation areas
Burkart et al (2018) analysed a field trial with two barley cultivars and two contrasting sowing densities in a random plot design, over two consecutive years, using aerial images of 28 flight campaigns. They concluded that aerial images collected by UAV can be used to provide quantitative data in crop management and precision agriculture
Summary
Agriculture is increasingly linked to information technology and automation, and in this context, the unmanned aerial vehicle (UAV) is a tool with many applications (Gómez-Candón et al, 2014). Jorge & Inamasu (2014) conducted a literature review on the use of UAVs in precision agriculture. They mention that Przybilla & Wester-Ebbinghaus (1979) did the first experiments involving using a UAV for photogrammetry. The combination of photogrammetry with UAVs appears to be a viable alternative for various agricultural applications (Vega et al, 2015; Khot et al, 2016; Hunt et al, 2018), and research on the use of UAVs for precision agriculture has increased considerably in recent years (Peña et al, 2013; Ballesteros et al, 2014; Torres-Sánchez et al, 2014; LópezGranados et al, 2015)
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