电子测量与仪器学报

范保江,孙磊,何杨帆,范晓飞,李玉超,索雪松.基于机器视觉技术的稻米等级快速自动判定方法及系统研究[J].电子测量与仪器学报,2022,36(10):123-130

基于机器视觉技术的稻米等级快速自动判定方法及系统研究

Research on the rapid automatic determination method and system ofrice grade based on machine vision technology

DOI：

英文关键词:rice roughness convolutional neural network machine vision fast detection

基金项目:国家自然科学基金（32072572）、河北省人才支持基金（E2019100006）、河北省重点研发计划（20327403D）、河北农业大学人才引进计划（YJ201847）、河北省高校科技研究项目（QN2020444）资助

作者	单位
范保江	1.河北农业大学机电工程学院
孙磊	1.河北农业大学机电工程学院
何杨帆	1.河北农业大学机电工程学院
范晓飞	1.河北农业大学机电工程学院
李玉超	1.河北农业大学机电工程学院
索雪松	1.河北农业大学机电工程学院

Author	Institution
Fan Baojiang	1.School of Mechanical and Electrical Engineering, Hebei Agricultural University
Sun Lei	1.School of Mechanical and Electrical Engineering, Hebei Agricultural University
He Yangfan	1.School of Mechanical and Electrical Engineering, Hebei Agricultural University
Fan Xiaofei	1.School of Mechanical and Electrical Engineering, Hebei Agricultural University
Li Yuchao	1.School of Mechanical and Electrical Engineering, Hebei Agricultural University
Suo Xuesong	1.School of Mechanical and Electrical Engineering, Hebei Agricultural University

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中文摘要:

当前稻米等级的判定多依赖于人工挑拣称重计算,具有人工主观性强、检测效率低等缺陷,因此实现快速自动判定稻米等级是稻米行业的必然趋势。本文基于机器视觉技术设计并开发了稻米等级快速自动判定系统。通过成像技术获取稻米籽粒高分辨率图像,利用 Watershed 算法和自适应阈值函数对图像进行处理,对不同籽粒进行标记并运用卷积神经网络训练,选取最优训练模型对糙米分类,利用线性回归分析数据,实现对稻米等级的判定。本系统与人工对同一批稻米等级的判定结果相似度可达 91. 4%,采用本方法设计的系统在对稻米等级判定的过程中不仅排除了人为的主观性,还在检测速度上有了显著提升, 从而提高了稻米分级判定效率。

英文摘要:

The determination of the current rice grade mostly relies on manual picking and weighing, the discrimination process has defects such as strong manual subjectivity, slow detection speed and low efficiency. Therefore, it is an inevitable trend in the rice industry to realize rapid and automatic determination of rice grades. Based on machine vision technology, this paper designs and develops a rapid automatic determination system for rice grades. This system obtains images of rice grains with high-resolution through imaging technology, uses Watershed algorithm and adaptive threshold function to process the images, marks different grains and uses convolutional neural network training, selects the optimal training model to classify brown rice, Use linear regression to analyze the data to realize the judgment of rice grade. It has been proved by experiments that the similarity between the system and the artificial judging results of the same batch of rice can reach 91. 4%. The system designed by this method not only eliminates the human subjectivity in the process of judging the rice grading, but also detects the speed that has been significantly improved, thereby improving the efficiency of rice grading and judgment.

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