## 1 Introduction  
Thanks to their simple maneuverability, high agility, and cost-effectiveness, unmanned aerial vehicles (UAVs) are assuming a growing importance in agricultural applications including yield estimation, precision irrigation, and crop surveillance [1, 2, 3, 4]. Path loss (PL) is a crucial large-scale parameter that has a substantial influence on the communication quality between ground stations and UAVs. Different from traditional terrestrial communications, the UAV-aided air-to-ground (A2G) channel demonstrates obvious three-dimensional (3D) characteristics [5, 6], and the PL in agricultural scenarios also has some unique characteristics due to the influence of different farmland materials on the reflection or scattering process. Therefore, the modeling and accurate prediction of PL is of great significance in the construction and enhancement of UAV communication systems under agricultural scenarios. Traditional PL predictions are primarily categorized into deterministic methods [7, 8, 9, 10, 11, 12, 13] and empirical methods [14, 15, 16, 17, 18, 19, 20, 21]. For deterministic methods, PL is typically modeled through the ray tracing (RT) technique combined with environmental information. The authors in [7, 8] utilized the RT technique to model the PL of urban scenarios, taking into account the effects of transceiver locations and antenna heights, respectively. The authors in [9; 10] predicted the PL under urban and forest scenarios, respectively, and both considered the additional impact of vegetation attenuation. For agricultural scenarios, the authors in [12] analyzed the channel characteristics of the narrowband internet of things under a typical rural scenario at 900 MHz by using the RT technique; they obtained the average PL between transceivers and derived the channel parameters including angular and delay spread. The authors in [13] developed a channel model for UAV-aided A2G radio frequency energy transfer based on the RT principle, which considered the influence of vegetation growth with time on PL in agricultural scenarios. Deterministic methods are generally considered to have a high accuracy. However, due to the complexity of ground materials caused by different types of crops in agricultural scenarios, there are inevitable differences between the reconstructed map materials and the real environment, which will affect the prediction accuracy. Moreover, deterministic methods normally come with high computational complexity. Empirical methods usually model PL as a multi-factorial equation regarding some channel parameters including arrival angle, path delay, and so on. For example, the authors in [14; 16] modeled the PL in scenarios involving rice and millet fields, respectively, as well as corn and soybean fields, and studied the effects of different crop growth stages on the PL. The authors in [17] measured the PL in a citrus plantation, and analyzed the measurement results based on the two-ray model, close-in (CI) free space reference distance PL model, and so on. For A2G scenarios, the authors in [20] investigated the effects of different transmission antenna heights on PL in rural scenarios at 26 GHz using a crane as a research platform, and analyzed the fitting performance of different empirical models, such as the CI and alpha-beta-gamma models. The authors in [21; 22], respectively, studied channel parameters such as the received signal strength indicator of ZigBee and the WiFi link in farmland scenarios based on UAVs. Although empirical methods are relatively simple, they have poor scenario dependence and usually demand a substantial volume of measured data to calibrate the model. With the increase in scenario types, it is sometimes challenging to derive a general analytical equation. Machine learning (ML) technology has attracted more and more interest in the field of channel parameter prediction due to its powerful data analysis ability [23; 24; 25; 26; 27; 28; 29].