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基于微带微波传感器的谷物含水率便携式检测装置的开发和应用

Development and application of portable measuring device for grain moisture content based on microstrip microwave sensor
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摘要 目的开发一种基于微带微波传感器的谷物含水率便携式测量装置。方法该装置由微波电路模块和实时测量模块组成,可实时显示含水率测量结果。选择水稻样品的厚度和环境温度来优化设备参数。采用随机森林、决策树、K-最近邻和支持向量机4种算法预测水稻的含水率。结果选择微波衰减、相移和温度作为随机森林算法模型的输入时,预测结果显示出最好的准确性和稳定性,最大平均绝对误差为0.55%,最大标准偏差为0.41%。结论该仪器结构简单、操作方便、成本低。有望为其他农产品的实时测量提供重要参考,对智能农业装备的发展具有重要意义。 Objective To develop a portable measuring device for grain moisture content based on microstrip microwave sensors.Methods The device was composed of microwave circuit modules and real-time measurement modules,and the moisture content measurement results could be displayed in real-time.The thickness of paddy rice samples and the ambient temperatures were chosen to optimize device parameters.The 4 kinds of algorithms of random forest,decision tress,K-nearest neighbor,and support vector machine were applied to predict the moisture content in the paddy rice.Results Microwave attenuation,phase shift and temperature were selected as the input of random forest,the results showed best accuracy and stability with maximum mean absolute error 0.55%and the maximum standard deviation 0.41%.Conclusion The instrument has the advantages of simple structure,convenient operation,and low cost.This work is expected to provide an important reference for the real-time measurement of other agricultural products,and of great significance for the development of intelligent agricultural equipment.
作者 刘锦 裘姗姗 周洁 韦真博 LIU Jin;QIU Shan-Shan;ZHOU Jie;WEI Zhen-Bo(School of Electronic Information,Hangzhou Dianzi University,Hangzhou 310018,China;School of Materials and Environmental Engineering,Hangzhou Dianzi University,Hangzhou 310018,China;School of Biosystems Engineering and Food Science,Zhejiang University,Hangzhou 310058,China)
出处 《食品安全质量检测学报》 CAS 北大核心 2022年第17期5485-5494,共10页 Journal of Food Safety and Quality
基金 国家重点研发计划项目(2016YFD070190402)。
关键词 微波传感器 微波透射法 谷物 含水率 随机森林算法 microwave sensor microwave transmission method grain moisture content random forest algorithm
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