## 2 Survey for Outdoor Agricultural UAV Applications
### Remote Sensing

Table 1: _Cont._
\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}  & \multicolumn{3}{c}{**Purpose of Study**} \\ \hline  & & & & & & & & \\  & & & & & & & & \\
11 & [PERSON] et al. [58] & \begin{tabular}{c} An adaptive approach for \\ UAV-based pesticide spraying \\ in dynamic environments \\ \end{tabular}  & Spraying & 2017 & Pesticide & \ \begin{tabular}{c} Single \\ UAV \\ \end{tabular}  &
\begin{tabular}{c} A computer-based system that controls a UAV for \\ precise pesticide deposition in the field and \\ metaheuristic route-planning method based on \\ particle swarm optimization, genetic algorithms, \\ hill-climbing and simulated annealing was evaluated \\ for autonomous adaptation of route changes. The \\ spray deposition was tracked by sensors, and the \\ system surrounded by wireless sensor networks \\ (WSN). The proposed system resulted in less \\ environmental damage, more precise changes in the route of flight and more accurate deposition of the \\ pesticide. \\ \hline
12 & [PERSON] et al. [59] & \ \begin{tabular}{c} Experimental evaluation of \\ UAV spraying for patch trees \\ of different shapes: Effects of \\ operational parameters on \\ droplet distribution \\ \end{tabular}  & Spraying & 2020 & Peach trees &
\begin{tabular}{c} The effects of UAV operational parameters on \\ droplet distribution for \\ which was \\ aerial spraying of \(\gamma\)-shape and CL-shape peak trees, \\ and improvement on the droplet coverage was \\ shown by the increase in nozzle flow rate at the end \\ of the study. \\ \hline
13 & \begin{tabular}{c} [PERSON], \\ [PERSON], [PERSON], [PERSON], [PERSON] \\ and [PERSON] [80] \\ \end{tabular}  & \begin{tabular}{c} Recognition of banana \\ fusarium with based on UAV \\ remote sensing \\ \end{tabular}  & 2020 & Panama & \ \begin{tabular}{c} Single \\ Bangana \\ \end{tabular}  &
\begin{tabular}{c} Beamar tsunami with disease was identified with \\ red-edge band multispectral camera sensor. The \\ UAV barrier logistic representation method used used to establish the spatial relationships between infested \\ plants and non-infested plants on the known map. \\ \hline
14 & \begin{tabular}{c} Wheat growth monitoring and \\ yield estimation based on \\ vehicle \\ \end{tabular}  & Crop & 2020 & Wheat & \begin{tabular}{c} This study was performed on wheat trials treated \\ with scoring densities and different nitrogen levels \\ multispectral camera mounted on the UAV. Multiple \\ linear regression (MLR), similar linear regression (LR), partial least squares regression (PTSR), stepwise multiple linear regression (SML), random \\ forest (RF) and artificial neural network (ANN) \\ modeling methods were used to estimate wheat \\ yield. The experimental results showed that machine \\ learning methods had a better performance for \\ predicting wheat yield. \\ \end{tabular}  & \begin{tabular}{c} \begin{tabular}{c} This study was performed on wheat trials treated \\ with scoring densities and different nitrogen levels \\ in the area. The images were collected by a \\ multispectral camera mounted on the UAV. Multiple \\ linear regression (MLR), partial least squares regression (PTSR), stepwise multiple linear regression (RTD), random \\ forest (RF) and artificial neural network (ANN) \\ modeling methods were used to estimate wheat \\ yield. The experimental results showed that machine \\ learning methods had a better performance for \\ predicting wheat yield. \\ \end{tabular}  & \begin{tabular}{c} \begin{tabular}{c} \begin{tabular}{c} \begin{tabular}{c} \hline \hline \hline \multicolumn{1}{c}{**No**} \\ \hline \hline \multirow{2}{*}{**No**} & **Study** & **Study Name** & **Task** & **Year** \\ \hline \multirow{5}{*}{11} & \multirow{5}{*}{[PERSON] et al.