基于机器视觉的谷物品种识别研究进展  

Research progress of cereal variety identification based on machine vision

作　　者：陈卫东[1,2] 范冰冰 王莹 刘超 李宛玉 CHEN Weidong;FAN Bingbing;WANG Ying;LIU Chao;LI Wanyu(College of Information Science and Engineering,Henan University of Technology,Zhengzhou 450001,China;National Engineering Research Center of Grain Storage and Logistics,Henan University of Technology,Zhengzhou 450001,China)

机构地区：[1]河南工业大学信息科学与工程学院,河南郑州450001 [2]河南工业大学粮食储运国家工程研究中心,河南郑州450001

出　　处：《河南工业大学学报（自然科学版）》2024年第1期133-142,共10页Journal of Henan University of Technology：Natural Science Edition

基　　金：财政部和农业农村部国家现代农业产业技术体系资助项目(CARS-03)。

摘　　要：品种纯度是谷物种子重要的质量指标,种子质量安全直接关乎国家粮食安全。国标规定的品种纯度鉴定采用形态鉴定法和苯酚染色法,鉴定结果受制于检验人员的经验且耗时较长。近年来,机器视觉技术和机器学习、深度学习算法发展迅速,在谷物品种识别和纯度、净度检测中取得了较大进展。主要从图像采集、图像预处理以及机器学习、深度学习技术在谷物品种识别领域的应用等方面进行归纳,分析了目前取得的研究成果以及存在的问题,对该领域未来研究重点进行了展望。Variety purity is a crucial cereal seed quality indicator,and the safety and quality of seeds are directly tied to the security of the nation′s food supply.The national standard for variety purity identification uses the phenol staining and morphological identification methods,and the results of the identification are time-consuming and subject to the inspectors′level of knowledge.Recently,cereal variety recognition,purity detection,and clarity detection have significant development,which is facilitated by machine vision technology,machine learning,and deep learning algorithms.The current research findings and issues in the areas of image acquisition,image pre-processing,machine learning,and deep learning technology in the field of cereal variety identification are summarized and analyzed in this review,and a forecast on the future research priorities in this area is also provided.

关 键 词：谷物品种识别 机器视觉 机器学习 深度学习 

分 类 号：TS210.1[轻工技术与工程—粮食、油脂及植物蛋白工程]