The traditional constant false alarm rate (CFAR) method, with fixed parameter settings and single noise background calculation, is unable to intelligently catch the current detection background. To improve the performance of the CFAR method, this paper proposes a target detection method based on decision tree classification (DTC) for high-frequency surface wave radar (HFSWR). Firstly, the training sample set and labels are obtained by means of a ship automatic identification system (AIS). Then, feature vector of range dimension, Doppler dimension and range-Doppler (RD) dimension is extracted by way of cell averaging, ordered statistics, censored mean and trimmed mean. Finally, DTC is used to recognize “true” and “false” targets in feature space. Experimental results show that, under the same number of detection targets, the DTC method is superior to traditional CFAR methods, and the accuracy of target detection can be increased by more than 5%.

Effective stored-grain insect pest detection is crucial in grain storage management to prevent economic losses and ensure food security throughout production and supply chains. Existing detection methods suffer from issues such as high labor costs, environmental interference, high equipment costs, and inconsistent performance. To address these limitations, we proposed PDA-YOLO, an improved stored-grain insect pest detection algorithm based on YOLO11n which integrates three key modules: PoolFormer_C3k2 (PF_C3k2) for efficient local feature extraction, Attention-based Intra-Scale Feature Interaction (AIFI) for enhanced global context awareness, and Dynamic Multi-scale Aware Edge (DMAE) for precise boundary detection of small targets. Trained and tested on 6200 images covering five common stored-grain insect pests (Lesser Grain Borer, Red Flour Beetle, Indian Meal Moth, Maize Weevil, and Angoumois Grain Moth), PDA-YOLO achieved an mAP@0.5 of 96.6%, mAP@0.5:0.95 of 60.4%, and F1 score of 93.5%, with a computational cost of only 6.9 G and mean detection time of 9.9 ms per image. These results demonstrate the advantages over mainstream detection algorithms, balancing accuracy, computational efficiency, and real-time performance. PDA-YOLO provides a reference for pest detection in intelligent grain storage management.

The precise measurement of wheat moisture content is a key issue to ensure the quality and safety of wheat. However, high-precision, rapid, and non-destructive detection of wheat moisture content remains challenging. In this paper, we proposed a system with single-layer low-frequency metasurface plane wave lens antenna for non-destructive wheat moisture detection in the frequency range of 5.8GHz - 5.9GHz. Based on the propagation characteristics of plane waves in multilayer dielectric, antenna and container error de-embedding algorithms are proposed to reduce the measurement errors introduced by the parameters of the antenna and container during the measurement process. According to the de-embedded scattering parameters of wheat, its dielectric constant and loss factor are obtained. The calculated dielectric constant and loss factor showed a good liner relationship with moisture content of wheat at 25°C(±0.5°C). Based on the relationship between the dielectric properties and moisture content of wheat, a linear regression equation is established to predict the moisture content of wheat. Experimental results show that the root mean square error, mean absolute error and maximum relative error of the predicted values were 0.19%, 0.16% and 4.0%.