基于主成分分析及LVQ神经网络的番茄种子品种识别  

Tomato seed varieties recognition based on principal component analysis and LVQ neural network

作　　者：赵学观[1,2] 王秀[1,2] 李翠玲[1,2] 高原源 王松林[1,2] 冯青春[1,2] 

机构地区：[1]北京农业智能装备技术研究中心,北京100097 [2]国家农业智能装备技术研究中心,北京100097 [3]中国农业大学信电学院,北京100083

出　　处：《浙江农业学报》2017年第8期1375-1383,共9页Acta Agriculturae Zhejiangensis

基　　金：国家高技术研究发展计划(2013AA102406);北京市农林科学院青年基金项目(QNJJ2017)

摘　　要：提出了一种基于主成分分析优化(PCA)及竞争性神经网络(LVQ)的番茄种子品种识别方法,对番茄品种识别技术与算法进行了研究,提取了番茄种子的几何特征、纹理特征和7个不变矩特征,通过主成分分析选取了4个主成分作为人工神经网络的输入,对黑迪、红迪、佳粉十八、金迪、丘比特5个品种进行了LVQ神经网络品种识别试验。试验结果表明,竞争层节点数目为20,训练次数为96时每粒种子识别的平均耗时最短,识别准确率最高,分别为0.2 s、90.5%,基于机器视觉的番茄种子品种识别与检测方法是可行的。In order to realize the real-time,accurate and no-damage mechanization identification of tomato seed varieties,according to the characteristics of tomato seeds and its image,the tomato varieties identification technology and algorithm were studied.This paper proposed a tomato seed varieties identification method,which is a kind of optimization by LVQ neural network based on principal components analysis,extracting the shape characteristics,texture feature and seven moment invariants of the tomato seeds.Four principal components as the input of artificial neural network were chosen through the principal components analysis.The identification test was conducted on five varieties of Heidi,Hongdi,Jiafen 18,Jindi and Cupid.The number of competitive layer neurons and training trials were determined according to the test,which were 20 and 96.Under the condition,the average time of each seed identification was the shortest,and the recognition accuracy was the highest,which were 0.2s and 90.5% respectively.The research showed that the method of identification and detection of tomato seed varieties based on machine vision is feasible.

关 键 词：番茄种子 品种识别 计算机视觉 神经网络 

分 类 号：S126[农业科学—农业基础科学]