## 1 Introduction
For example, the authors in [24] evaluated the prediction accuracy of an artificial neural network (ANN) and various ML methods on PL by combining the measurement data in rural scenarios, and studied the impact of various neuron quantities and neural network (NN) layers on prediction performance. The authors in [25] utilized ML regressions to establish a PL model and investigated the impact of crop height and density at different growth stages of paddy and sugarcane on PL. The authors in [26] modeled the PL in different vegetation regions such as orange, cherry, pine, and walnut by using several ML methods such as recurrent NN. The authors in [28] applied different ensemble models to predict PL in rural scenarios for the first time, and designed the best ensemble technique. The authors in [29] predicted the PL in rural scenarios by using the k-nearest neighbor and random forest methods, and compared the predicted results with the conventional particle swarm optimization algorithm. ML methods typically demand a substantial volume of measured data during the training stage, which are rarely applied in rapidly moving UAV-aided A2G scenarios. In the case of insufficient measurement data, RT simulation data are usually employed as a substitute. Due to the complexity of ground materials and the limitation of map reconstruction accuracy in agricultural scenarios, there may be a certain disparity between RT simulation data and the actual results, which causes a decrease in the accuracy of the prediction results and greatly increases the limitations of NN applications. This study endeavors to fill this gap. The innovations and contributions are outlined as follows. * A novel ML-based PL model with double-weight-neurons for UAV-aided A2G communications under agricultural scenarios is firstly proposed in this paper. The proposed model takes into account the main factors affecting PL, including signal propagation distance, UAV height, and carrier frequency, and can accurately predict the PL of the line-of-sight (LoS) and non-LoS (NLoS) paths in the scenarios. * A new ANN structure named double-weight neurons-based ANN (DWN-ANN) is designed for the PL model, which can solve the problem of large measurement data requirements in traditional ANNs and achieve accurate PL prediction through two-step training. Moreover, an RT pre-correction module is introduced to solve the problem of insufficient RT simulation accuracy caused by complex ground materials in agricultural scenarios. * Channel measurement campaigns are carried out over a farmland area with different ground materials at 3.6 GHz. The measurement data are obtained for the training and validation of the proposed model. Moreover, the ground material parameters for RT simulations are modified. The prediction results demonstrate a fine concordance with the obtained data and achieve higher accuracy compared to the empirical models, which indicates that the proposed model can accurately predict the PL under agricultural scenarios. The rest of the paper is structured as follows. Section 2 proposes the ML-based PL model under agricultural scenarios. In Section 3, the PL prediction scheme and the details of the DWN-ANN are introduced. Section 4 provides the channel measurement campaigns and a verification and comparison of the proposed PL model. Finally, the conclusions are summarized in Section 5.