## 1 Introduction  
An unmanned aerial system (UAS) or unmanned aerial vehicle (UAV) is defined as a powered aerial vehicle that does not carry a human operator, uses aerodynamic forces to provide vehicle lift, and can fly autonomously or be remotely piloted aircrafts. In this last case, they are also called a remotely piloted aerial system (RPAS) (i.e., aerial platform (RPA) plus control station for pilot and operator (if applicable) actions [1; 2; 3]). UASs and RPASs (usually the same denomination is employed, even in the everyday jargon they are called drones) are mostly associated with military, industry, and other specialized operations, but with recent developments in the area of sensors and information technology in the last two decades, the scope of drones has been widened to other areas like agriculture [4]. The technology of RPASs for areas such as precision agriculture is drawing increasing attention from different sectors interested in seeking real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19]. Traditional imagery, provided, for example, by RGB (read green blue) and/or NIR (near infared) sensors, has proven to be useful in many agroforestry applications. However, it lacks the spectral range and precision to profile materials and organisms that only hyperspectral sensors can provide. This kind of high-resolution spectroscopy was firstly used in satellites and later in manned aircraft, which are significantly expensive platforms and extremely restrictive due to availability limitations and/or complex logistics. More recently, UASs have emerged as a very popular and cost-effective remote sensing technology, composed of aerial platforms capable of carrying small-sized and lightweight sensors. Meanwhile, hyperspectral technology developments have been consistently resulting in smaller and lighter sensors that can currently be integrated in UASs for either scientific or commercial purposes. The hyperspectral sensors' ability for measuring hundreds of bands raises complexity when considering the sheer quantity of acquired data, whose usefulness depends on both calibration and corrective tasks occurring in pre- and post-flight stages [15]. Independent of the aerial platform, sensors play an important role in data acquisition. According to [20], in which an extensive work addressing hyperspectral technology can be found, there are four main techniques for acquiring measurable data from a given target: By hyperspectral imaging, multispectral imaging, spectroscopy, and RGB imagery. The most significant differences are synthetized in Table 1, which considers not only the comparison carried out by [20] but also the vision of [PERSON] and [PERSON] [21], who stated that imaging sensors for remote sensing can be divided into the method by which they achieve (1) spatial discrimination and (2) spectral discrimination. Regarding the aerial platform, different types of RPAS models which have been used in the last two decades are shown in Table 2[22]. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Spectral Information** & **Spatial Information** \\ \hline Hyperspectral Imaging & *** & *** \\ Multispectral Imaging & *** & *** \\ Spectroscopy & *** & * \\ RGB Imagery & * & *** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Differences between hyperspectral and multispectral imaging, spectroscopy, and RGB (read green blue) imagery. A classification based on a bullet rate (1–3) was used to quantify both the spectral and spatial information associated to each acquisition technique, in relative terms. Fixed-wing (Figure 1a) RPASs are entirely different in their design compared to multi rotors, and aerodynamic shape of two wings provides an easy glide for RPAs. The single-rotor helicopter (Figure 1b) is a model with just one big-sized rotor on top and one small-sized rotor on the tail of the RPA.