A Low-Cost Antipodal Vivaldi Antenna-Based Peanut Defect Rate Detection System

Yuanyuan Yin, Fangyan Ma, Xiaohong Liu, Shuhao Wang, Junjie Xia and Liqing Zhao ()
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Yuanyuan Yin: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
Fangyan Ma: Shandong Peanut Research Institute, Shandong Academy of Agricultural Sciences, Qingdao 266100, China
Xiaohong Liu: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
Shuhao Wang: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
Junjie Xia: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
Liqing Zhao: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China

Agriculture, 2025, vol. 15, issue 7, 1-20

Abstract: 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 (S 11 and S 21 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 R 2 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.

Keywords: defect rate; peanut pod; vivaldi antenna; nondestructive testing (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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