# Hybrid Sensing Platform for IoT-Based Precision Agriculture
## 2 Related Work in IoT-Based Sensing for PA  
PA has been pervasively used to improve farm management processes from both animal [4; 5] and crop [1] perspectives. From the animal farming perspective, PA enables us to capture and analyse data regarding animals' behaviour and to find correlations between animals' behaviour, living environment, and farm productivity [4]. From the crop perspective, which is the main focus of this research, PA enables accurate data collection regarding crop, soil, and weather to improve the farm yield while minimising the use of required resources. In the past decade, the emergence of IoT has revolutionised the data collection process in the PA context. IoT-based PA enables us to reduce human interaction with the farm management process while at the same time increasing the accuracy of data collection and data analysis [6]. Integration of IoT in the PA context has been the focal point of multiple studies. Some researchers focus on the IoT architecture required to implement an IoT-driven PA system [6; 7; 8; 9; 10; 11]. They proposed four [6; 7; 8; 11], five [10], and six [9] layer IoT architectures. From their work, it can be perceived that there are four main layers in an IoT-based PA architecture, which are device, network, cloud, and application, where some of these works break down some layers into sublayers to further discuss the purpose of each layer [9; 10]. Practical implementation of IoT-based PA has been also discussed in several related works. Some of these works focus on an IoT-enabled ground-based sensing approach to improve the animal farm management process. [PERSON] et al. [5] investigated the efficiency of using Radio Frequency Identification (RFID) tags in comparison with traditional approaches for monitoring milk production throughout lactation in sheep flocks. Such data-driven analysis of animals' behaviour can result in improvements in animal farm \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Sensing Approach** & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{Aerial} & \(\bullet\) & High coverage due to & \(\bullet\) & Limited flight time \\  & & mobility & \(\bullet\) & Non-continuous data \\  & \(\bullet\) & Low cost due to lower & \(\bullet\) & Lower data quality due to \\  & & number of sensing nodes & & UAV movement and \\  & & required & & vibration \\ \hline \multirow{4}{*}{Ground-based} & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{High data accuracy due to } & \(\bullet\) & High cost of procurement, \\  & & & & deployment and maintenance \\ \cline{1-1}  & & sensor proximity to plants & \(\bullet\) & Low coverage due to \\ \cline{1-1}  & & \(\bullet\) & Continuous data collection & \\ \cline{1-1}  & & & \(\bullet\) & Prone to environmental \\ \cline{1-1}  & & & & damages \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of IoT-based precision agriculture sensing approaches. productivity while at the same time reducing the required resources. Another application of wireless sensing networks in animal farming is the capability to develop smart traceability systems, which can be remarkably beneficial for brand and origin protection in animal farming products [12]. The ground-based sensing approach has been also used pervasively for the crop management process. [PERSON] et al. [13] developed an IoT-enabled ground-based sensing solution for the real-time visualisation of soil moisture and temperature that was used to apply timely variable-rate irrigation. [PERSON] et al. [14] designed an IoT-based platform called SmartFarmNet, which can automate the collection of environmental, soil, fertilisation, and irrigation data from ground-based sensors. SmartFarmNet automatically correlates such data and filters out invalid data from the perspective of assessing crop performance. It also computes crop performance forecasts and provides personalised crop recommendations for any farm. [PERSON] [15] focused on time correlation between sensors, as environmental factors can cause drift in sensors' clocks and this can reduce data accuracy.