## 2 Related Works
The study compares modified models of exponential decay for vegetation environments, including Weissberger, ITU-R, and COST235, along with a linear logarithmic model, through Matlab simulations. An ideal fitting model, namely the parametric exponential decay model (OFPED), is obtained. Measurements are conducted in a wheat field using Zigbee technology, with a transmission power of 0 dBm and a 4 dB gain omnidirectional antenna. RSSI and Packet Loss Rate (PLR) are obtained at different distances from the transceivers and various heights of the transmitter and receiver antennas, considering three stages of wheat growth. The findings reveal that the RSSI decreases as the distance from the transceiver increases, and the PLR increases with the distance from the transceiver. Additionally, path loss decreases with increasing antenna height, and the path loss at 2.4 GHz is higher than that at 433 MHz. Finally, the validation results indicate that modified exponential decay (MED) models can be used as conservative upper and lower limits for path loss, at least for the wheat field. In [25], the study focuses on deploying WSN in high grass environments. Received signal strength (RSS) measurements were collected on an 80 m \(\times\) 80 m high grass farm, where the transmitter and receiver were always in line-of-sight (LOS). The transmission antenna was placed at the center of the grassy area, and the receiving antennas were positioned at eight different distances. These antennas' height was 20 cm, with omnidirectional radiation patterns. Data collection was carried out at a frequency of 1925 MHz, at a total of 128 measurement points, with 300 RSS samples collected at each point. As a result, it was possible to propose an empirical propagation loss model for this type of scenario, which was compared to classical models such as FSPL and Two-Ray Ground, both of which were less accurate in predicting RSS in high grass environments. The article [26] provides a systematic literature review of propagation models for WSN in vegetated environments, including references to articles such as [20; 22; 23; 24; 25]. The review indicates that, from 2011 to 2021, Zigbee technology was preferred for such environments, but currently, the use of LoRa technology is on the rise. Furthermore, the article suggests employing techniques not previously used in wireless communication propagation studies in these scenarios, such as machine learning. In addition, the article [27] provides an overview of recent developments in radio wave propagation modeling using ML algorithms. It emphasizes the importance of selecting the appropriate ML method for the propagation model accuracy and notes the significant number of propagation modeling articles that have utilized artificial neural networks (ANNs). The article anticipates a further growth in this trend, highlighting the challenges, prospects, and open issues in this research direction. Some related articles cited, such as [28], employ the back-propagation (BP) neural network to predict the received signal power in a suburban environment. This model is based on field measurements obtained from the base station (BS) and the receiver (Rx), including information on topography, frequency, transmitted power, antenna angle, and received power at all locations. The results demonstrate that the proposed model accurately predicts the received signal power for this type of environment. The article [29] uses a combination of three techniques: multidimensional regression based on ANN, analysis of variance based on Gaussian process, and feature selection aided by principal component analysis (PCA), and path loss data measured in a suburban area in Korea are used. The collected data show that the proposed combined path loss and shading model is more accurate and flexible compared to the conventional log-normal path loss model.