# Hybrid Sensing Platform for IoT-Based Precision Agriculture
## 4 Results
### Data Validation
#### 4.1.1 Aerial Temperature and Humidity Data Validation Using Ground-Based Data--Experiment 1  
In this research, the Ecowitt WH2910 weather station was used in ground-based node. With the assumption that the data obtained from the weather station are accurate, its data are considered as ground truth. In this experiment, the aerial temperature and humidity sensor data are compared with the ground-based data to analyse the impact of UAV movement on such data. Each cycle of aerial data collection was 10 min, and in each cycle, three temperature and humidity data were collected from the weather station. Therefore, each aerial cycle's data are divided into three sections, and from each section, the aerial minimum, maximum, and average value are calculated and compared with ground-based data. The average value is calculated by adding the minimum and maximum values and then dividing this by two ((maximum + minimum)/2). In the following section, the temperature and humidity of all aerial data collection cycles are analysed in comparison with the weather station data. As the data visualisation in Figure 5 shows, although the temperature data of the aerial node reflect the same pattern as the ground-based data, there are differences between the values at any specific point. The first observation is that the aerial node always gives a higher temperature value compared with the ground-based data, except at two instances in cycle 5 point 3 and cycle 11 point 3, where the difference is very small. In addition, data visualisation shows that aerial minimum temperature data are always closer to the ground-based data compared with aerial maximum and average temperature data  
As Table 3 shows, the correlation coefficient between the aerial minimum temperature and ground-based temperature is 0.97 and is higher than the aerial average and aerial maximum temperature. This indicates that the aerial minimum temperature has the strongest linear relationship compared with the ground-based temperature and therefore has higher validity to be selected as aerial temperature data. Another data analytics model that we used to determine the validity of the aerial data was a linear regression supervised machine learning model. Linear regression enables us to analyse how aerial data can be used to predict the ground-based data and therefore reflects the validity of aerial data. Table 3 also shows aerial temperature linear regression R\({}^{2}\) values with the ground-based temperature. Similar to the correlation coefficient, the aerial minimum temperature data have also a higher R\({}^{2}\) value than the aerial maximum and average temperature. The R\({}^{2}\) value, which is 0.93, indicates a strong linear relationship. This shows that the aerial minimum temperature provides more valid data compared with aerial average and maximum temperature data. The same analysis process was performed on aerial and ground-based humidity observations to analyse the validity of the aerial node humidity data. Figure 6 shows the humidity data analysis. Comparison between aerial and ground-based humidity data shows that, contrary to temperature, where aerial data were always higher than the ground-based data, in humidity, the aerial data are always lower than the ground-based data, except at two instances in cycle 5 point 3 and cycle 11 point 3, where the difference is very small. In the humidity data, the aerial maximum reading is always closer to the ground-based humidity. Table 4 shows the correlation coefficient and linear regression of aerial and ground-based humidity data.