## 1 Introduction  
Recently, research on the Internet of Things (IoT) applications in agriculture has significantly increased [1; 2; 3; 4]. IoT-based agriculture can be categorized into [1]: smart irrigation, soil monitoring, nutrient management, precision farming, smart livestock management, crop monitoring, and farm produce storage. In smart irrigation, intelligent systems are developed for providing sufficient water to crops while minimizing water waste. These processes can be remotely monitored and controlled via the IoT. In soil monitoring, IoT sensors measure soil parameters such as pH, soil moisture, and nutrients. In nutrient management, usage of the soil nutrients nitrogen, phosphorus, and potassium (NPK) is controlled such that an appropriate dosage is provided in consideration of the economic and environmental costs. The IoT collects and analyzes data in precision farming to increase efficiency and productivity. Multi-sensors monitor a cow's body temperature and movement in smart livestock management. Data analysis is then performed to maximize milk production. In crop monitoring, the IoT sensors can detect plant diseases. In farm produce storage, the temperature and humidity of storage units are monitored by sensors placed within. This work deals with some of the previously described categories, namely monitoring of plant leaf temperature and environmental parameters. Both leaf temperature and environmental parameters (air and soil) are essential for plant growth. Leaf temperature is indicative of the photosynthesis rate, leaf water loss, and stomatal conductance of a plant [5]. Leaf temperature can be measured using invasive and non-invasive sensors [6]. Since invasive sensors are impractical, a non-invasive method, such as using thermal cameras, is preferred [7; 8; 9; 10; 11; 12; 13]. A FLIR Vue Pro R thermal camera was used to measure the grapevine's leaf temperature for calculating the crop water stress index (CWSI) [7]. The system added an RGB camera to capture the visible image, in which the image processing technique was employed to extract the leaf area. Then, the average temperature measured by the thermal camera was computed in the leaf area to calculate the CWSI. In [8], a FLIR P660 thermal camera was used for measuring the strawberry leaf surface temperature to define the temperature difference with the ambient air temperature in the night frost conditions. In [9], the FLIR C2 thermal camera was used to measure the leaf temperature of the cowpea plant. The temperature of the centric trifoliate leaf was extracted using the camera software to create the datasets. The datasets were compared with the data from the leaf porometer sensor to measure the leaf stomatal conductance. The thermal cameras in [7; 8; 9] are standalone or hand-held thermal cameras that are suitable for offline leaf temperature measurement, where the thermal images taken by the cameras are processed using a personal computer in an offline manner. The embedded thermal cameras are suitable for online leaf temperature monitoring, where the cameras are interfaced with the embedded processing for data collecting and processing. In [10], an embedded thermal camera FLIR Lepton 3.5 combined with the RGB camera was used to measure the canopy temperature of almond trees. Both cameras were connected to a Raspberry Pi module, which performed the image segmentation of the RGB image to find the leaf area and calculate the leaf temperature from the corresponding pixels in the thermal camera. The thermopile array was used to measure the leaf temperature of tomato plants in a smart greenhouse [12]. The leaf temperature was used to control the windows and heating of the smart greenhouse for minimizing the crop temperature stress. In [13], a low-cost MLX90620 thermal camera was used to measure the olive tree canopy temperature for estimating the water status. The thermal camera was connected to an Arduino Mega, which processed and displayed the temperature.