## 2 Survey for Outdoor Agricultural UAV Applications
### Remote Sensing
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No** & **Study** & **Study Name** & **Task** & **Year** & **Product/Focus** & \
\begin{tabular}{c} **UAV** \\ **Type** \\ \end{tabular} &
\begin{tabular}{c} **Purpose of Study** \\ \end{tabular} \\ \hline
6 & \begin{tabular}{c} **Mappping forest tree species in** \\ **high-resolution UAV-based** \\ **RG-imagery by means of** \\ **convolutional neural networks** \\ \end{tabular} & Mapping & 2020 & Forest tree species & \
\begin{tabular}{c} **Single** \\ **UAV** \\ \end{tabular} &
\begin{tabular}{c} **RGB imagery taken from a UAV was assessed with the learning capabilities of convolutional neural networks (CNN) and a semantic segmentation approach (U-Net) for the mapping of tree species in the forest environment. Nine tree species, deadwood, three grass-level classes and forest floor were accurately and quickly mapped. \\ \hline
7 & \begin{tabular}{c} **[PERSON] et al.** [54] \\ **the meeting and mapping tree** \\ **seedling in UAV imagery** \\ **networks and field-verified data** \\ \end{tabular} & Mapping & 2020 & Tree seedlings & \
\begin{tabular}{c} **Single** \\ **UAV** \\ \end{tabular} &
\begin{tabular}{c} **RGB imagery taken from a UAV was assessed with the learning capabilities of convolutional neural networks (CNN) and a semantic segmentation approach (U-Net) for the mapping of tree species in the forest environment. Nine tree species, deadwood, three grass-level classes and forest floor were accurately and quickly mapped. \\ \hline
7 & \begin{tabular}{c} **[PERSON] et al.** [54] \\ **the meeting and mapping tree** \\ **seedling in UAV imagery** \\ **networks and field-verified data** \\ \end{tabular} & Mapping & 2020 & Tree seedlings & \
\begin{tabular}{c} **Single** \\ **UAV** \\ \end{tabular} &
\begin{tabular}{c} **RGB imagery taken from a UAV was assessed with the learning capabilities of convolutional neural networks (CNN) and a semantic segmentation approach (U-Net) for the mapping of tree species in the forest environment. Nine tree species, deadwood, three grass-level classes and forest floor were accurately and quickly mapped. \\ \hline
8 & \begin{tabular}{c} **[PERSON] et al.** [54] \\ **the meeting and mapping tree** \\ **seedling in UAV imagery** \\ **networks and field-verified data** \\ \end{tabular} & Path planning & 2020 & Exotic pests & \
\begin{tabular}{c} **Single** \\ **UAV** \\ \end{tabular} &
\begin{tabular}{c} **A UAV-based coverage algorithm was proposed to cover all areas and to detect ecoule passdang the area. The capsule position sites were calculated in the whole environment and generated a path for the cup distribution location of the UAV in the algorithm. This planned distribution was more advantageous and preferable than a zigzag distribution in this study. \\ \hline
9 & \begin{tabular}{c} **Sensor Planning for a** \\ **Synthetic UAV and UGV** \\ **Systems for Precision** \\ **Agriculture** \\ \end{tabular} & Remote sensing & 2016 & \
\begin{tabular}{c} **Nitrogen level** \\ **prediction** \\ \end{tabular} &
\begin{tabular}{c} **This study aimed to predict the nitrogen (N) map of an environment and to plan an optimum with a depth fertilizer with UAV. A UAV labeled to measure each point visited by the UAV. The total time spent was minimized according to traveling and measuring. They applied the method of the traveling salesperson problem with neighborhoods (TSIM) for this path-minimization problem.** \\ \hline
10 & \begin{tabular}{c} **Enfects of citrus tree-shape and** \\ **s spraying height of small** \\ **unmanned aerial vehicle on** \\ **droplet distribution** \\ \end{tabular} & \begin{tabular}{c} **S**raying \\ **2016** \\ \end{tabular} & \
\begin{tabular}{c} **S**raying \\ **UAV** \\ \end{tabular} &
\begin{tabular}{c} **The effects of spraying height of a UAV and attrac tree shape were investigated for droplet distribution \\ **in this study. The UAV performance at a 1.0 m** \\ **working height was better than the other heights.** \\ **Additionally, to increase the droplet distribution, open center shape citrus trees were advised based on the results of the study.** \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}