Plant protection UAVs are becoming the preferred plant protection method for agricultural pest control. At present, the evaluation of droplet distribution in aerial spraying is collected and evaluated after the completion of prevention and control operations, and there is a lack of real-time evaluation methods. Based on the flight parameter during the UAV plant protection process, real-time estimation of droplet distribution is the key to solving this problem and further improving the effectiveness of aerial spraying. This study proposes a merging algorithm for arbitrary polygonal regions, meshing the boundaries of the region, divide the mesh segments based on the overlapping meshes between the two regions, and connect the valid mesh connection segments of the two regions according to certain rules to obtain the intersection, union, and residual operation results between the regions. Afterwards, software based on this algorithm was developed and applied to generate spraying coverage regions, leakage spray regions, and repeated spray regions. The experimental results on theoretical and irregular routes show that the algorithm can accurately generate droplet distribution regions. The error of the calculation results with a mesh scale of 0.05 m is within 7‰, and the operating speed is above 30 Hz, meeting the real-time requirements. The smaller the mesh scale is, the higher the accuracy of the calculation results is, but the slower the calculation speed. Therefore, in practical applications, it is necessary to choose an appropriate mesh scale based on hardware computing power and accuracy level requirements. This study solves the problem of cumulative calculation of droplet distribution during the operation of plant protection UAVs, providing a basis for objectively evaluating the operation quality of plant protection UAVs and optimizing the setting of operation parameters.

Soil moisture content plays a crucial role in precision agriculture and geological hazard monitoring, driving the need for stable, reliable, and high-precision sensors. Capacitive soil moisture sensors based on Frequency Domain Reflectometry (FDR) are widely adopted due to their favorable measurement performance, yet their accuracy is highly susceptible to environmental interferences such as temperature, salinity (electrical conductivity), and soil type. This paper systematically reviews current calibration strategies addressing these three factors, classifying them into hardware-based compensation and software-based calibration (including conventional mathematical and machine learning models). Furthermore, it critically analyzes the trade-offs of these approaches in terms of robustness, scalability, and field applicability. To break through current technical limitations, this review argues that future research must prioritize the physical decoupling of multi-parameter interferences under extreme conditions. Additionally, to overcome the generalization crisis of current data-driven models, adaptive strategies utilizing techniques like transfer learning are essential. Finally, implementing Edge-AI on resource-constrained hardware is crucial for achieving calibration-free or real-time online calibration strategies, ensuring long-term measurement accuracy.

LiDAR-based 3D object detection remains challenging due to sparse and irregular point cloud distributions, which degrade detection accuracy for small and occluded objects. In view of this, this paper proposes a novel two-stage voxel-based 3D detector, namely LSCA-RCNN, to address these issues. First, spatial residual blocks (SRBs) and large-kernel spatial-wise convolutions are integrated into the 3D backbone to suppress feature degradation and to expand the receptive fields for stable multi-scale feature learning. Second, a ConvNeXt-based 2D backbone with spatial attention is constructed to enhance discriminative feature representation of small objects. Third, a cascaded detection head embedded with fine-grained grouped convolutions and cross-stage cross-attention is designed to achieve progressive bounding box refinement and to improve localization precision. Extensive evaluations on the KITTI dataset with the R40 metric show that the proposed method achieves consistent performance improvements over the baseline. In the moderate setting, LSCA-RCNN increases the 3D AP by 2.12%, 7.66%, and 5.43% for cars, pedestrians, and cyclists, respectively, while achieving gains of 1.62%, 5.05%, and 7.05% under the hard setting. These results validate the effectiveness and robustness of the proposed LSCA-RCNN for complex and challenging autonomous driving detection tasks.

LiDAR point clouds are sparse, irregular, and unevenly distributed, which makes representative feature extraction challenging for 3D object detection. Currently, Mamba modules have been increasingly applied to 3D object detection due to their ability to efficiently model global spatial dependencies. However, preserving geometric structures while modeling global spatial dependencies remains challenging in Mamba-based frameworks. In view of this, this paper proposes a single-stage 3D object detection framework for LiDAR-based point clouds. First, this paper designs a hierarchical multiscale structure called Multiscale Voxel–Point Alternating Fusion (MVPF) Module. Within this module, the 2DMamba module is introduced into the feature extraction stage at each scale to model global spatial dependencies in the BEV plane through selective state-space scanning. Second, we design a Z-to-Channel cross-dimensional reorganization strategy that merges the voxel Z dimension into the channel dimension, yielding a BEV-form feature representation suitable for 2DMamba processing. Third, this paper proposes a Local Voxel Feature Enhancement (LVFE) module composed of a Point-to-Voxel Feature Aggregation (PVFA) module, a Voxel Densification Module (VDM), and a coordinate-indexed Voxel-to-Point Mapping (VPM) module, which enhances local voxel representations while maintaining point–voxel spatial correspondence. On the KITTI validation set, Mamba-BEV achieved higher detection accuracy and inference speed than the baseline. Under the Moderate difficulty level of AP_R11, the 3D AP for Car, Pedestrian, and Cyclist improved by 0.6%, 1.5%, and 0.4%, respectively, compared to the baseline, while the inference speed increased from 44.05 FPS to 67.11 FPS. These results demonstrate the effectiveness of the proposed Mamba-BEV for point cloud-based 3D object detection.

Reliable in situ soil moisture measurements provide essential information for irrigation scheduling and agricultural soil–water management. Among the available measurement techniques, capacitive soil moisture sensors based on frequency domain reflectometry (FDR) are widely used because of their high measurement accuracy and suitability for in situ monitoring. However, the resonant frequency of an FDR capacitive soil moisture sensor is affected simultaneously by temperature, soil electrical conductivity (EC), and water content conditions, and independent fixed coefficient corrections cannot adequately describe this coupled relationship. To address this problem, we developed a time division multiplexed three-parameter FDR sensor that uses a composite electrode to sequentially acquire a water content-related resonant frequency, a conductivity-sensitive voltage, and a temperature-sensitive voltage at the same sensing location. A joint three-parameter calibration model was then established to map the three outputs to the reference volumetric water content determined by oven drying. Comparison of five candidate calibration models showed that the backpropagation neural network (BPNN) provided the best overall performance on the development data. A paired 10-seed analysis showed no stable predictive advantage for WOA initialization over standard BPNN, so WOA was retained only as a secondary robustness analysis. After the WOA-BPNN calibration model was fixed and deployed on the embedded platform, repeat measurements under 72 temperature–salinity–water content conditions within the same prepared sandy loam system yielded an RMSE of 2.086 percentage points. Within the tested calibration domain, the coordinated three-parameter hardware and joint calibration model reduced water content errors associated with temperature and salinity variation. These repeat measurements were conducted in the same prepared sandy loam system and, therefore, do not demonstrate generalization across other soil textures, field sites, independently prepared sample batches, or seasons.

Magnetically coupled resonant wireless power transmission (MCR-WPT) is an ideal solution for underwater wireless power transmission (UWPT). However, due to the conductivity of seawater, eddy current loss significantly reduces system transmission efficiency. This study develops an analytical estimation method to derive explicit expressions for eddy current loss and transmission efficiency, characterize their dependence on key parameters, and analyze the resonance frequency characteristics of the MCR-WPT system under different conductivities. The optimal resonant frequency and transmission efficiency improvement under variable-conductivity underwater environments are investigated. First, the coil model is simplified to its equivalent form. Based on the Biot–Savart law, the magnetic field is calculated by integral operations, and an analytical model of the eddy current loss is formulated. Consequently, the expression for the system transmission efficiency in seawater at different depths is derived, and a specific resonance frequency is identified at which the efficiency attains its maximum value. An underwater coil model is established using Ansys Maxwell finite element analysis (FEA), and the effects of electrical conductivity and resonance frequency on eddy current loss and system efficiency are analyzed. Finally, an underwater experimental platform is constructed. A freshwater solution and seawater solutions with varying electrical conductivities are prepared using artificial sea salt and pure water; frequency-sweeping experiments are then conducted. The experimental results are in good agreement with the theoretical analysis and simulations, thereby validating the accuracy of the proposed model.