## 4 Solution Proposal for UAV Applications in Greenhouses
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No** & **Study** & **Information** & **Sensors** & **App. Environment** & **Result** \\ \hline \multirow{4}{*}{5} & & This study introduced the semi-direct visual odometry (SVO) algorithm, which is very fast and powerful. It eliminates feature extraction and matching techniques that reduce the speed of visual odometry. SVO combines the properties of feature-based methods (tracking multiple features, parallel tracking and mapping, keyframe selection) with the accuracy and speed of direct methods. & Monocular camera (VSLAM) & ROS & A successful real-time SLAM algorithm was realized by combining the advantages of direct and indirect SLAM algorithms. \\ \cline{1-1}  & & This study proposed a monocular visual-inertial system (VINS) for 6-degree-of-freedom (DoF) safe prediction using a camera and a low-cost IMU. The initialization procedure provides all necessary values, including pose, velocity, gravity vector, gyroscope deflection and 3D feature position, to bootstrap the best nonlinear & Monocular camera, IMU (VSLAM) & ROS & A successful VSLAM was achieved with efficient IMU pre-integration, automatic estimator initialization, online external calibration, error detection and recovery, loop detection and pose graph optimization. \\ \cline{1-1}  & & & (VSLAM) & ROS & online external calibration, error detection and recovery, loop detection and pose graph optimization. \\ \cline{1-1}  & & & & & and pose graph optimization. \\ \hline \multirow{4}{*}{6} & & This study performed the evaluation of open code VIO algorithms on flying robot hardware configurations. The methods used were multi-state constraint Kalman filter (MSCRF) [11], open keyframe-based visual-inertial SLAM (MVCS) [11], robust visual-inertial algorithm (OVOV) [12], monocular visual-inertial SLAM (VSSN-Mono) [10], semi-direct visual odometry (SVO) [11] & Matlab & The comparison revealed that SVO + MSF had the most accurate performance. In addition, processing performance. In addition, processing time per frame, CPU usage and memory usage criteria were taken into consideration in the study. \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \multirow{4}{*}{8} & & In this study, a new measurement model named & & & \\ \cline{1-1}  & & local-optimal-multi-state constraint Kalman filter (LOMSCKF) was designed. With this model, the nonlinear optimization method was fused with MSCRF to perform VIO. In addition, unlike MSCRF, all of the measurements and information available in the sliding window were used. & Monocular camera, IMU (VSLAM) & Matlab & The performance of the proposed LONSCKF was evaluated using both virtual and real-world datasets. \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._Considering the success of the methods in Table 3 and their practicality, it is clear that the problem of implementing a UAV that navigates autonomously in the greenhouse will be solved with modern SLAM techniques. However, an autonomous UAV in the greenhouse is also a challenge, as SLAM still does not achieve the desired level of success. However, different tasks can be performed by the UAV in a greenhouse with the existing SLAM methods. By using a UAV, which has the advantages of mobility, high-resolution imaging and low cost, the lack of studies on aspects such as imaging, yield estimation and mapping in the greenhouse environment can be eliminated by UAVs that collect data and perform monitoring in the greenhouse. It is possible to use SLAM methods to collect data and make observations autonomously from greenhouses where production occurs in all seasons. Moreover, in this big data era, it may become easier and faster to obtain large datasets. This could be an exciting advance for smart greenhouses.