基于机器视觉的番茄采摘器红外图谱识别研究  

The Study on Infrared Image Recognition for Tomato Picker Based on Machine Vision

作　　者：陈进熹[1] 丁洁瑾 Chen Jinxi;Ding Jiejin(Special Equipment Institute, Hangzhou Vocational and Technical College, Hangzhou 310018, China;Department of Mechanical and Electrical Engineering, Zhejiang Tongji Vocational College of Science and Technology,Hangzhou 311231,China)

机构地区：[1]杭州职业技术学院特种设备学院,杭州310018 [2]浙江同济科技职业学院机械与电气工程系,杭州311231

出　　处：《农机化研究》2022年第4期44-48,53,共6页Journal of Agricultural Mechanization Research

基　　金：浙江省水利厅科技计划项目(RC1813);浙江省高等教育“十三五”第一批教学改革项目(jg20180611)。

摘　　要：为了实现番茄分类自动化采摘,基于机器视觉和红外图谱技术设计了识别系统。基于可见光分析,分离番茄图像,对RGB和HSI通道强度进行分析,发现色调H可以有效区分除半熟和成熟阶段的番茄成熟度;引入红外图谱分析,采集810nm番茄图谱,发现灰度在半熟和成熟阶段区别明显。因此,建立半熟和成熟阶段区分模型,并以G、R、H、NIR强度以及4个因素标准方差为系统输入,基于色调H处理番茄图像,采用聚类算法计算番茄中心和半径。对成熟度判定与番茄半径精度进行测试,结果表明:成熟度分类准确率在94.8%以上,半径相对误差小于6%。Inorder to achieve the classified automatic picking,this system was designed based on infrared image recognition and machine vision.The RGB and HSI grayscale analysis were done in visible band by detached tomato image.The result showed that grayscale of hue H could classify the Maturity of harvesting-tomato except semi mature and mature period.There was a obviously different in grayscale of infrared image in 810nm between semi mature period and mature period.The model for distinguish the semi mature period and mature period was achieved by grayscale of G,R,H and NIR,the standard deviation of grayscale of G,R,H and NIR.Based on hue H,the core and radius of tomato were calculated by clustering algorithm.Tests for maturity differentiation and radius of tomato were taken,the result showed that accuracy for the maturity differentiation was above 94.8%,and radius error of tomato was less than 6%.

关 键 词：番茄 采摘器 红外识别 图像识别 成熟分类 

分 类 号：S225[农业科学—农业机械化工程] TP391.41[农业科学—农业工程]