## 2 Survey for Outdoor Agricultural UAV Applications
### Remote Sensing
\begin{table}
\begin{tabular}{c 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} & \multicolumn{3}{c}{**Purpose of Study**} \\ \hline
1 & \begin{tabular}{c} [PERSON], [PERSON], \\ [PERSON], [PERSON], \\ [PERSON] \\ and [PERSON] [31] \\ \end{tabular} & \begin{tabular}{c} Identifying and mapping \\ individual plants in a highly \\ diverse high-level/scientivess system using UAV imagery \\ and deep learning \\ \end{tabular} & Mapping & 2020 & Fraligones & \begin{tabular}{c} Single \\ UAV \\ \end{tabular} & \
\begin{tabular}{c} In this study, frailigones plants were classified from \\ UAV images using a newly proposed S8 Heus U-Net \\ deep learning method. Later, the proposed model \\ was compared with other deep learning-based \\ semantic segmentation methods and was shown to be superior to these methods. \\ \end{tabular} &
\begin{tabular}{c} **Purpose of Study** \\ \end{tabular} \\ \hline
2 & \begin{tabular}{c} [PERSON], [PERSON], \\ [PERSON], [PERSON], [PERSON], \\ [PERSON], [PERSON] \\ and [PERSON] [32] \\ \end{tabular} & Mapping & 2020 & Macadama tree & \begin{tabular}{c} Single \\ UAV \\ \end{tabular} & \
\begin{tabular}{c} In this study, frailigones plants were classified from \\ UAV images using a newly proposed S8 Heus U-Net \\ deep learning method. Later, the proposed model \\ was compared with other deep learning-based \\ semantic segmentation methods and was shown to be superior to these methods. \\ \end{tabular} &
\begin{tabular}{c} **Purpose of Study** \\ \end{tabular} \\ \hline
3 & \begin{tabular}{c} [PERSON], [PERSON], \\ [PERSON], [PERSON], [PERSON], \\ [PERSON], [PERSON] \\ and [PERSON] [32] \\ \end{tabular} & Mapping & 2018 & \begin{tabular}{c} Agricultural \\ underground \\ infrared imagery: A case study \\ \end{tabular} & \
\begin{tabular}{c} Single \\ UAV \\ drainage systems \\ \end{tabular} &
\begin{tabular}{c} The PitAJ software and PitAJ-DmPaper Pro were \\ employed to determine drainage pipe locations \\ using visible (VIS), thermal infrared (THB) and near-infrared (NIR) imagery by UAV. The study claimed that TRI imagery from UAV has \\ considerable potential for detecting drain line \\ locations under dry-surface conditions. \\ \end{tabular} \\ \hline
4 & \begin{tabular}{c} [PERSON] et al. \\ [51] \\ \end{tabular} & Mapping & 2017 & Winter wheat & \begin{tabular}{c} Data from sensors such a light detection and ranging (LIDAR), global navigation satellite system (GNES) and inertial measurement unit (IMU), mounted on UAV travel based to conduct mapping \\ of winter wheat field. IMU, GNSS and UAV data \\ were used to estimate the orientation and position (pose). The point cloud data from LDAR were \\ combined with the estimated pose for three-dimensional (3D) mapping. \\ \end{tabular} & Mapping & 2017 & Winter wheat & \
\begin{tabular}{c} Single \\ UAV \\ \end{tabular} &
\begin{tabular}{c} Point independent classification algorithms derived from the random forest algorithm were tested for the creation of over medaps. Input data were collected using a low-cost RGB camera mounted on a UAV. \\ The automatic object-based classification algorithm \\ had the highest classification accuracy with an \\ overall accuracy of 89.0\%. \\ \end{tabular} \\ \hline
5 & \begin{tabular}{c} [PERSON] et al. \\ [52] \\ \end{tabular} & \begin{tabular}{c} An automatic method for \\ weed mapping in out fields \\ based on UAV imagery \\ \end{tabular} & Mapping & 2020 & Weed & \
\begin{tabular}{c} Single \\ UAV \\ \end{tabular} &
\begin{tabular}{c} Four independent classification algorithms derived from the random forest algorithm were tested for the creation of over medaps. Input data were collected using a low-cost RGB camera mounted on a UAV. \\ The automatic object-based classification algorithm \\ had the highest classification accuracy with an \\ overall accuracy of 89.0\%. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}