# Hybrid Sensing Platform for IoT-Based Precision Agriculture
## 4 Results
### Data Calibration
#### 4.2.1 Aerial Temperature and Humidity Data Calibration Using Ground-Based Data--Experiment 4  
As discussed in experiment 1 of data validation, aerial minimum temperature and aerial maximum humidity readings have a correlation coefficient of 0.97 and 0.98, respectively, with ground-based data, which shows a very strong linear relationship. Therefore, we consider these two data sets for our calibration process. Although aerial minimum temperature and aerial maximum humidity have a strong linear relationship with ground-based data, temperature readings were always above the ground-based readings and aerial humidity readings were always below the ground-based readings. In this experiment, we use a linear regression equation to calibrate the aerial data to reduce their distance from ground-based data. In order to do so, first, we need to obtain the intercept (a) and slope of regression line (b) values. Executing linear regression analysis on aerial and ground-based data provides these values, which are represented in Table 6.  
Based on the obtained data in Table 6, the linear regression equation was applied on both aerial temperature and humidity data points to obtain the calibrated data sets. Figures 13 and 14 show the temperature and humidity data comparison with ground-based data before and after calibration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Aerial and Ground-Based Data** & **Intercept** & **Line Slope** &
\begin{tabular}{c} **Euclidean Distance** \\ **Before Calibration** \\ \end{tabular} \\ \hline Temperature & 1.63 & 0.80 & 14.50 \\ Humidity & 6.51 & 0.95 & 37.09 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Aerial temperature and humidity intercept, line slope, and Euclidean distance before calibration.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Aerial Spectrometer Channel Data** & \
\begin{tabular}{c} **Linear Regression with Corresponding** \\ **(Predictor Variable)** \\ \end{tabular} &
\begin{tabular}{c} **Ground-based Spectrometer Channel Data** \\ **(Coefficient of Determination/R2)** \\ \end{tabular} \\ \hline Channel E (Green) & 0.56 \\ Channel H (Yellow) & 0.46 \\ Channel O (NIR) & 0.52 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Aerial green, yellow, and NIR channel linear regression with corresponding ground-based channel data.  
As Figure 13 shows, the aerial calibrated temperature is closer to the ground-based temperature. The Euclidean distance between the aerial original temperature and ground-based temperature was 14.5, and after calibration, it is reduced to 7.06. This shows a 51% improvement in the aerial temperature data accuracy. This distance can be considered as the closest possible distance to be obtained using the linear regression model.  
Similar to temperature, the humidity calibration data illustrated in Figure 14 also show an improvement in reducing the distance between aerial original humidity and ground-based humidity data. The Euclidean distance between these two data sets was 37.09 before calibration. After calibration, this distance is reduced to 24.27, which shows a 34% improvement in aerial humidity data accuracy.