# Hybrid Sensing Platform for IoT-Based Precision Agriculture
## 4 Results
### Data Validation
#### 4.1.2 Aerial Infrared Data Validation Using Ground-Based Data--Experiment 2  
Two main variables that can impact the aerial sensors' data quality are UAV movement and vibration. Since similar infrared sensors are deployed on both the aerial and ground-based nodes to obtain the plant temperature and there is no vibration on the ground-based node, therefore, the ground-based infrared data can be used as ground truth to analyse the data fluctuation between the aerial and ground-based nodes. During this experiment, the ground-based sensor was reading data from a single plant and the aerial sensor was reading data from different areas of the experimental field. However, due to the fact that the density of plants was high in the experimental field and the entire field was covered with plants of a similar type, it can be assumed that the UAV has been constantly reading data from similar types of plant in similar environmental conditions compared with the target plant. The only points at which the UAV has captured invalid data are during take-off and landing, where the UAV is on the ground. These data points are removed from the data set. In this experiment, the infrared data of all 13 aerial data collection cycles are compared with ground-based data of the same time span. The total number of data points is 1080 for each aerial and ground-based data set, which are used in the correlation and linear regression analysis.  
As is shown in Figure 7, the aerial infrared data show more fluctuations compared with ground-based infrared data. This indicates that the UAV movement and vibration can impact the data quality obtained from this sensor through aerial observation. In order to determine the validity of the data, the correlation coefficient between aerial and ground-based data was calculated, which is equal to 0.91.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Aerial Humidity**} & **Correlation Coefficient with Ground-Based Humidity** &
\begin{tabular}{c} **Linear Regression with Ground-Based Humidity** \\ **(Coefficient of Determination/R\({}^{2}\))** \\ \end{tabular} \\ \hline Minimum & 0.88 & 0.77 \\ Average & 0.94 & 0.88 \\ Maximum & 0.98 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Aerial humidity correlation coefficient and linear regression with ground-based humidity.  
Figure 8 shows the scatter plot between aerial and ground-based data points. As the correlation coefficient value and scatter plot show, there is a strong linear relationship between aerial and ground-based data. In order to analyse the possibility of using aerial data to predict ground-based data, the linear regression analysis is performed on both aerial and ground-based data sets.  
Figure 9 shows the linear regression pattern where ground truth data are predicted based on aerial data. The R\({}^{2}\) value is 0.83, which shows strong linear regression between the two data sets. This indicates that aerial infrared data are valid and there is a possibility for data calibration to improve the aerial data quality.  
Figure 8: Scatter plot of aerial infrared data and ground-based infrared data.  
Figure 7: Aerial infrared data compared with ground-based infrared data.  
Figure 9: Linear regression pattern between aerial infrared data and ground-based infrared data.