## 1 Introduction  
In the past few years, one of the largest industries in the world, the agriculture sector, has faced many challenges, such as providing more and better food due to the exponential growth of the world population (from 1.65 billion in 1900 to 10.1 billion in 2050 [1, 2, 3, 4]), climate change (by affecting the crop production by 2030 [5, 6]), and the depletion of limited natural resources. Precision agriculture (PA), also known as smart agriculture, based on the Internet of Things (IoT), is seen as a transforming force that will play a vital role in the further development of agriculture in the coming decades [7, 8, 9]. Smart agriculture uses cutting-edge technologies (such as IoT, 5G and wireless sensor networks (WSNs)) for real-time monitoring of plant growth and health and environment parameters aiming to improve crop production, while at the same time conserving natural resources and reducing greenhouse gas emissions [10, 11, 12, 13, 14]. For example, with real-time data on soil moisture levels, temperature, pH, and humidity, farmers can reduce the risk of crop diseases and root rot, as well as prevent waterlogging and soil compaction. Plant disease diagnosis is another area where real-time monitoring offers significant benefits. For instance, biosensors enable the early detection of airborne pathogens, allowing farmers to take quick action and minimize crop losses. IoT-based real-time data collection requires deploying many sensor nodes in vegetation fields that are spatially distributed and interconnected wirelessly. Each node consists of a sensor, a microcontroller for signal processing, a transceiver with an antenna, and a source of energy such as a battery or energy harvester (solar power or radio frequency wave energy) [15; 16; 17]. The sensor nodes are connected to a server through a gateway (base station) to exchange, process, and store the collected data using the Internet environment [17; 18; 19]. For supporting real-time data flow in such networks, it is essential to ensure a reliable and resilient wireless connection between the nodes (sensor(s) and gateway) [20], as the dynamics of the radio channel directly impact data loss and delay during node-to-node data exchange [21; 22; 23]. Furthermore, in IoT-based smart agriculture systems, radio wave propagation faces unique challenges (such as attenuation in vegetation and soil and multiple reflections) because of sensor nodes deployed in agriculture fields at or slightly above the ground level. For instance, to gather real-time data on soil moisture levels, temperature, pH, etc., the communication nodes should be positioned in rows alongside the crop on the ground or elevated up to 0.5 m above ground to reduce the risk of node damage by tractors and implements. Hence, there is a need for extensive measurement companies to study radio wave propagation in vegetation fields and find the model(s) that best represent the propagation characteristics in the communication scenarios of smart agriculture. Pioneering measurements were conducted at 2.4 GHz to study radio wave propagation in corn fields [22; 24; 25]. A distance from 70 m up to 100 m was defined as a stable communication distance between the sensor node and the base station at 2.4 GHz at a fixed base station antenna height (1 m, 2 m, and 3 m) [22] and in obstructed line-of-sight (O-LoS) scenarios, i.e., vegetation between Tx-Rx in LoS. Moreover, in most previous studies of radio wave propagation in vegetation environments, the antenna of the sensor node (i.e., antenna in the vegetation field) has been placed between 0.9 m and 3 m [22; 24; 26; 27] above the ground. However, this placement does not allow for a proper evaluation of the actual characteristics of wireless channels for real Smart Agriculture scenarios for near-ground communications [28].