# Hybrid Sensing Platform for IoT-Based Precision Agriculture
## 2 Related Work in IoT-Based Sensing for PA
This paper proposed a sink node's clock as a reference to automatically synchronise IoT devices across the farm. [PERSON] et al. [16] collected soil, crop, and weather data and used an IoT platform to analyse, visualise, and correlate data that can trigger an irrigation system. [PERSON] et al. [17] proposed an IoT-based irrigation system that reduces the use of fresh water while maintaining constant soil moisture across an entire farm. They deployed ground-based sensors to collect soil moisture, environmental temperature and humidity, carbon dioxide level, and daylight intensity, and also used external weather forecast data (including sunrise time and ultraviolet index) to predict the soil moisture in the next hour. [PERSON] et al. [18] used Bluetooth as a communication technology for both end nodes' microcontrollers, as well as a host station to collect soil moisture and activate an irrigation system for growing Romaine lettuce. [PERSON] et al. [19] used an in-field sensing station to measure soil moisture and temperature and also air temperature. They send this data to a base station, where meteorological information is also collected from a weather station. Such a combination of real-time in-field data and predictive data of future weather conditions provides remarkable information for an efficient and effective irrigation approach. [PERSON] et al. [20] used a pheromone trap, which uses certain chemical substances to live pests. After pests are trapped inside the pheromone trap, RGB images are captured. Images are then processed using a Convolutional Neural Network (ConvNet) to detect pests and identify their species. [PERSON] [21] used optical sensors to analyse plant health status. She argues that the interaction of leaf tissue with light depends on structural and leaf chemical properties. As a result, when disease emerges in crop, the pathogen results in a change in leaf structure and chemicals, and this phenomenon results in light spectrum reflectance compared to a healthy crop. Such sensors can be mounted on UAVs to analyse the plant health status [22] of an entire farm, which is discussed via the field experiment results in Section 4. UAVs play a major role in PA and have different applications, including communication, data transfer, actuation, and sensing [22; 23; 24]. As a platform for aerial sensing, UAVs provide the best balance between image/data quality, coverage, and operating cost compared to helicopters and airplanes [25]. [PERSON] et al. [26] collected complementary data from UAV and ground-based sensors and used an IoT platform to perform data fusion and data analysis. They argued that an efficient UAV trajectory along with IoT data fusion can improve farm management accuracy. [PERSON] et al. [27] proposed an IoT-based PA management system called PLATEM PA, which uses a mesh network of sensors and actuators. PLATEM collects data from UAV and also ground-based sensors to improve fertilisation and irrigation. [PERSON] et al. [28] focused on IoT-based aerial sensing, where they used a multi-rotor UAV to provide real-time hyperspectral images from a farm. Such an IoT-based aerial sensing approach enables real-time monitoring of the plant health status of an entire farm using only one sensor node deployed on the UAV. [PERSON] et al. [29] proposed an IoT solution that uses ground-based soil sensors, complementary UAV-based aerial sensors, and external data from the U.S. Department of Agriculture's soil database to improve farm irrigation and fertilisation. [PERSON] et al. [30] analysed crop health status variations using aerial and ground-based sensing. However, they did not investigate the integration of these two sensing approaches to improve the aerial data quality. Other researchers have also investigated aerial and ground-based sensing but the ground and aerial data in these works were not related [7; 27; 31]. Furthermore, they did not investigate or discuss any aspect or potential benefits of integrating these approaches.