The rapid and nondestructive detection of tea leaf moisture content (MC) is of great significance to processing tea with an automatic assembly line. This study proposes an MC detection method based on microwave scattering parameters (SPs). Through the established free-space electromagnetic measurement device, 901 different frequency points are taken between 2.45 and 6 GHz using a vector network analyzer (VNA). The SPs of tea leaves with different moisture contents (5.72–55.26%) at different bulk density and different sample thicknesses were measured. The relationship between frequency, S₂₁ amplitude and moisture content, thickness, and bulk density of tea was analyzed using correlation coefficients, significance analysis, and model construction. Back propagation (BP) neural network, decision tree (DT), and random forest (RF) MC prediction models were established with the frequency, amplitude, and phase of the SPs, thickness, and bulk density of the samples as inputs. The results showed that the RF-based model had the best performance, with determination coefficient (R²) = 0.998, mean absolute error (MAE) = 0.242, and root mean square error (RMSE) = 0.614. Compared to other nondestructive testing processes for tea, this method is simpler and more accurate. This study provides a new method for the detection of tea MC, which may have potential applications in tea processing.

Peanut quality, with the defect rate as a critical determinant, has a profound impact on its market value. In this study, we introduce an innovative non-destructive evaluation method for peanut defects. Differing from traditional and often expensive or complex detection methods, our approach utilizes a low-cost antipodal Vivaldi antenna, complemented by a custom-designed defect rate detection system. Prior to experimentation, we simulated the antenna and system architecture to ensure their operational efficiency, a step that not only conserves resources but also validates the reliability of subsequent results. We conducted experimental tests on fresh peanut pods, obtaining electromagnetic scattering parameters (S₁₁ and S₂₁ magnitudes/phases within 1–2 GHz) through non-destructive measurements. These parameters were used as input features, while the defect rate served as the output variable. By implementing the XGBoost algorithm, we established predictive models for defect rate quantification (regression) and defect grade classification. In comparison to some traditional statistical models, such as linear regression, which may struggle with non-linear data patterns, XGBoost effectively modeled the complex relationship between the scattering parameters and the defect rate. Experimentally, the regression model achieved an R² value of 0.8113 for defect rate prediction, and the classification model reached an accuracy of 0.7526 in grading defect severity. The entire device, costing less than USD 50, provides a significant cost advantage over many commercial systems. This low-cost setup enables real-time evaluation of peanut pod defects and efficiently categorizes the defect rate without the time-consuming sample preparation and tiling operations required by traditional image-based inspection methods. As a result, it offers an affordable and practical solution for quality control in peanut production, showing great potential for wide application in the peanut industry.