# Hybrid Sensing Platform for IoT-Based Precision Agriculture
## 4 Results
### Data Calibration
#### 4.2.2 Aerial Infrared Data Calibration Using Ground-Based Data--Experiment 5  
In validation experiment 2, we found that aerial infrared readings have a relatively high correlation with ground truth, with a correlation coefficient value of 0.91 and linear regression R\({}^{2}\) value of 0.83. The validation analysis was conducted on the entire data of aerial cycles and the corresponding ground truth data. However, in the calibration process, other variables, such as time, solar radiation, wind speed, and wind gust, and their impacts on collected data are also taken into consideration to improve the data quality. Such analysis helps to identify which of these variables impact the aerial data readings and how factoring out some of the data that are highly impacted by such variables can improve the aerial data quality. The first step in calibrating aerial infrared sensor readings is to analyse these sensor readings at different times of the day. As discussed before, the aerial data were captured three times a day at morning, afternoon, and evening. Therefore, comparing the cycles that were captured in the morning with the afternoon and evening cycles can determine which time of the day yielded the highest correlation between aerial and ground-based data and also which time provides better data for calibration using linear regression. Figure 14: Original and calibrated aerial humidity data compared with ground-based data. Figure 13: Original and calibrated aerial temperature data compared with ground-based data. As Table 7 shows, the evening cycles provide more accurate data, followed by morning cycles. Morning and evening cycles' correlation and linear regression values are slightly different; however, the afternoon cycles' correlation and linear regression values are remarkably lower compared with morning and evening cycles. Although afternoon cycles show significantly different correlations between aerial and ground-based data compared with evening cycles, these two cycles have a relatively similar wind speed and wind gust average. This indicates that the wind speed in the existing threshold does not impact the infrared sensor readings deployed on the UAV. Regarding solar radiation, it can be seen that solar radiation in afternoon cycles is remarkably higher compared with morning and evening cycles. This can be a possible reason for the reduction in correlation between aerial and ground-based data. However, the solar radiation value in evening cycles is also higher than morning cycles but, contrary to afternoon cycles, evening cycles show higher accuracy compared with morning cycles. The reason for this can be due to the fact that, based on aerial video recorded during all afternoon cycles, the sunlight was directly shining on the plant canopy and therefore causing a significant temperature difference between the areas of the plant where the aerial sensor was pointing and the areas where the ground-based sensor was pointing. Therefore, based on the obtained data, the aerial infrared is more reliable during evening cycles. As a result, we consider the evening cycles for the data calibration process. Based on the linear regression analysis on the evening data set, the intercept (a) value is 6.93 and the slope of regression line (b) is 0.76. The Euclidean distance between aerial and ground-based data before calibration is 84.05. This indicates a 66.1% improvement in aerial plant temperature data accuracy after calibration. Based on the obtained intercept and slope of regression line, the aerial data points were calibrated to reduce their distance from ground-based data. The aerial original plant temperature, aerial calibrated plant temperature, and ground-based plant temperature data are compared in Figure 15.  
As Figure 15 shows, the aerial calibrated plant temperature is closer to the ground-based data. The Euclidean distance between the aerial calibrated plant temperature and