The RF–Absolute Gradient Method for Localizing Wheat Moisture Content’s Abnormal Regions with 2D Microwave Scanning Detection

Dong Dai, Zhenyu Wang, Hao Huang, Xu Mao (), Yehong Liu, Hao Li and Du Chen
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Dong Dai: College of Engineering, China Agricultural University, Beijing 100083, China
Zhenyu Wang: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Hao Huang: College of Engineering, China Agricultural University, Beijing 100083, China
Xu Mao: College of Engineering, China Agricultural University, Beijing 100083, China
Yehong Liu: College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300384, China
Hao Li: College of Engineering, China Agricultural University, Beijing 100083, China
Du Chen: College of Engineering, China Agricultural University, Beijing 100083, China

Agriculture, 2025, vol. 15, issue 15, 1-22

Abstract: High moisture content (MC) harms wheat storage quality and readily leads to mold growth. Accurate localization of abnormal/high-moisture regions enables early warning, ensuring proper storage and reducing economic losses. The present study introduces the 2D microwave scanning method and investigates a novel localization method for addressing such a challenge. Both static and scanning experiments were performed on a developed mobile and non-destructive microwave detection system to quantify the MC of wheat and then locate abnormal moisture regions. For quantifying the wheat’s MC, a dual-parameter wheat MC prediction model with the random forest (RF) algorithm was constructed, achieving a high accuracy (R 2 = 0.9846, MSE = 0.2768, MAE = 0.3986). MC scanning experiments were conducted by synchronized moving waveguides; the maximum absolute error of MC prediction was 0.565%, with a maximum relative error of 3.166%. Furthermore, both one- and two-dimensional localizing methods were proposed for localizing abnormal moisture regions. The one-dimensional method evaluated two approaches—attenuation value and absolute attenuation gradient—using computer simulation technology (CST) modeling and scanning experiments. The experimental results confirmed the superior performance of the absolute gradient method, with a center detection error of less than 12 mm in the anomalous wheat moisture region and a minimum width detection error of 1.4 mm. The study performed two-dimensional antenna scanning and effectively imaged the high-MC regions using phase delay analysis. The imaging results coincide with the actual locations of moisture anomaly regions. This study demonstrated a promising solution for accurately localizing the wheat’s abnormal/high-moisture regions with the use of an emerging microwave transmission method.

Keywords: microwave localizing method; wheat’s moisture content; abnormal moisture region; machine learning; microwave transmission (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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