The detection of the moisture content of wheat is an important index used to measure the quality and preservation of wheat. In order to rapidly and non-destructively detect the moisture content of wheat, in this study, we designed a stripline detection device that measures 151 frequency points in the 50–200 MHz frequency range with a vector network analyzer. Random forest (RF), extreme learning machine (ELM), and BP neural network prediction models were established, using the frequency, temperature, volume density and dielectric constant as input and the water content as output. It was shown that, in the frequency range 50–200 MHz, the permittivity of wheat decreases as the frequency increases, and that this is negatively correlated. The dielectric constant of wheat increases as the moisture content, temperature, and bulk density increase, and these are positively correlated. The random forest (RF) prediction model, which uses the frequency, temperature, effective dielectric constant ${\epsilon }_{eff}$. and volume density as inputs and the wheat moisture content as the output, demonstrates the best performance. The determination coefficient (R²) = 0.99977, the mean absolute error (MAE) = 0.044368, the mean square error (MAE) = 0.0053011, and the root mean square error (RMSE) = 0.072809. This study provides a new device and method for the detection of the moisture content of wheat. The device is small and is not easily disturbed by the external environment. It can be measured in a variety of conditions and is important for the development of low-cost, high-precision, and portable devices for the detection of the moisture content of wheat.

In this study, a novel Vivaldi antenna with dimensions of 100 mm × 85 mm × 1.6 mm, designed for a moisture measurement system, is built to enhance the gain of conventional Vivaldi antennas in the low-frequency band to suit the needs of moisture detection. The fence structure and choke slot are modified to enhance the antenna’s radiation properties in the low-frequency band, and simulation is performed to determine how different structural parameters affect the antenna’s performance. The results show that in the frequency band of 5-6 GHz, the voltage standing-wave ratio (VSWR) of the antenna is less than 2, and the gain at 5.8 GHz reaches 16.2 dBi after installing the lens. Compared with conventional unmodified Vivaldi antennas, the gain at 5.8 GHz increases by approximately 6.11 dBi. The antenna is then processed and measured, and the measured results are in good agreement with the simulated results; hence, the antenna can be widely used in the field of moisture detection.

This study aimed to investigate the water dissipation pattern from peanut pods under natural drying conditions after harvest. The Shandong peanut Luhua 22 was used to examine the effects of varying moisture content, bulk density, and porosity on the relative permittivity of the peanut at a signal frequency of 5.8 GHz. The peanut dielectric constant, porosity, and bulk density were used as inputs and peanut kernel moisture as outputs. Support vector regression (SVR), extreme learning machine (ELM), sparrow search algorithm-support vector regression (SSA-SVR), and sparrow search algorithm-extreme learning machine (SSA-ELM) were used to create a prediction model of peanut kernel moisture content. The results show that the water content of peanut kernels decreased in a fast and then slow manner throughout the drying process and that the water content of kernels was stable at 5–8% at the end of drying. The relative permittivity of peanut kernels increased with an increase in the water content and bulk density but decreased with an increase in porosity. The developed SVR, ELM, SSA-SVR, and SSA-ELM water-content prediction models were validated and analyzed in this study, with the model test set coefficients of determination of 0.936, 0.949,0.984, and 0.994, respectively. In comparison to SVR, ELM, and SSA-SVR, the SSA-ELM root mean square error was reduced by 0.0080, 0.0060, and 0.0012, respectively. According to the findings, the ELM neural network model, which is based on the optimization of the SSA, has an improved prediction accuracy. This prediction model provides a theoretical foundation for the variations in peanut seed moisture content during the natural drying process after harvesting peanuts in Shandong, which will be useful for future peanut storage and transportation.

Accurate evaluation of peanut seedling emergence is critical for ensuring agronomic research accuracy and planting benefit efficiency, but traditional manual methods are limited by strong subjectivity and inconsistent batch inspection standards. In order to quickly and accurately evaluate the emergence rate and quality of peanuts, this study proposes an intelligent evaluation system for peanut seedling conditions, which is constructed based on an improved YOLOv11 combined with the Segment Anything Model (SAM) for peanut seedling emergence evaluation, using high-resolution images collected by Unmanned Aerial Vehicles as the data foundation. Experimental results show that the improved YOLOv11 model achieves a detection precision of 96.36%, a recall rate of 96.76%, and an mAP@0.5 of 99.03%. The segmentation performance of SAM is outstanding in terms of integrity. In practical applications, the detection time for a single image by the system is as low as 83.4 ms, and the efficiency of video counting is 6–10 times higher than that of manual counting. Without extensive data annotation, this method performs excellently in peanut seedling emergence quantity statistics and growth status classification, providing efficient, accurate technical support for refined peanut cultivation management and mechanical sowing quality evaluation.