# Hybrid Sensing Platform for IoT-Based Precision Agriculture
## 5 Discussion  
The HSP uses data observations of a small number of ground-based sensors to automatically validate, calibrate, and improve the accuracy of data observations collected by UAV-based sensors. Application of statistical data analysis and supervised machine learning shows that the correlation between aerial and ground-based data is strong in the case of humidity, temperature, and infrared plant temperature data, and it is also at a moderate level regarding spectrometer data. In addition, application of the linear regression line equation on aerial data resulted in reducing the Euclidean distance between aerial data and ground truth. Therefore, the derived formula can be used in future data collections to automatically improve the aerial data in comparison with ground-based data. The research findings show that using ground-based data as ground truth in several locations of a large-scale farm, and using the validated and calibrated aerial data, can potentially improve the data quality and farm management process. By using the HSP, the main limitation of the ground-based sensing approach, which requires a large number of sensing nodes, can be addressed. Instead, UAVs can be used to collect data from the entire farm and significantly reduce the number of ground-based sensors that are otherwise required. In addition, the UAV can only concentrate on the areas far from the ground-based nodes, requiring less time to collect data from the entire farm. Moreover, transferring the HSP data to an IoT platform enables real-time data collection and visualisation and also automated data analysis, which are very important for timely actuations in PA. Although HSPs have potential benefits in improving data quality and farm management processes, there are several limitations in their implementation and adoption. One of the limitations is that the accuracy of the optical sensors deployed on the aerial node can be impacted by UAV vibration. The exact level of data quality degradation due to UAV vibration cannot be assessed using the existing system, because such measurement requires comparison between UAV data and accurate aerial data that are not impacted by vibration. Such data can be obtained using robotics technology or by installing a sensor stabiliser on the UAV, which can be a direction for future research, to analyse the impact of vibration on data quality degradation. Nevertheless, in order to reduce the impact of UAV vibration on data quality, there is a need for moving the UAV with low speed and also maintaining the position of the UAV at different points of interest across the field to collect valid data with a minimum level of vibration. In this research, we used the UAV's \"positioned flight mode\" to automatically position the UAV at low altitude, with less than half a meter's distance from the plant. Positioned flight mode also stabilises the UAV against wind force and therefore reduces the vibration impact. Another limitation in this research was that, due to the global COVID-19 pandemic, the data collection was limited to four days in a small field, although the techniques for data collection and data analysis on larger-scale fields with a longer data collection duration will be the same as discussed in this research, but additional data collection with a larger number of ground-based nodes might provide better data training for the validation and calibration process. The exact  
Figure 18: Comparison of aerial and ground-based spectrometer green, yellow, and NIR channel data from target plant during morning cycles after calibration. number of ground-based sensors in order to have trusted results depends on two factors, which are the farm scale and the duration of data collection. Determining the number of ground-based nodes requires additional investigation using supervised machine learning techniques to determine which number of ground-based nodes and which duration of data collection can provide better data training to avoid underfitting or overfitting in the training model.