[1]曲歌,陈争光,张庆华.基于无信息变量消除法的水稻种子发芽率测定[J].江苏农业学报,2019,(05):1015-1020.[doi:doi:10.3969/j.issn.1000-4440.2019.05.002]
　QU Ge,CHEN Zheng-guang,ZHANG Qing-hua.Study on germination rate of rice seed based on uninformation variable elimination method[J].,2019,(05):1015-1020.[doi:doi:10.3969/j.issn.1000-4440.2019.05.002]
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基于无信息变量消除法的水稻种子发芽率测定()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2019年05期
页码:
1015-1020
栏目:
遗传育种·生理生化
出版日期:
2019-10-31

文章信息/Info

Title:
Study on germination rate of rice seed based on uninformation variable elimination method
作者:
曲歌1; 陈争光1; 张庆华2
(1.黑龙江八一农垦大学电气与信息学院，黑龙江大庆163319；2.大庆技师学院计算机工程系，黑龙江大庆163254)
Author(s):
QU Ge1; CHEN Zheng-guang1; ZHANG Qing-hua2
(1.College of Electrical and Information Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China;2.Department of Computer Engineer, Daqing Technician College, Daqing 163254, China)
关键词:
水稻种子; 近红外光谱; 蒙特卡洛交叉验证; 无信息变量消除法; 发芽率
Keywords:
rice seed;  near infrared spectroscopy;  Monte Carlo cross validation;  uninformation variable elimination;  germination rate
分类号:
S339.3+1；S511
DOI:
doi:10.3969/j.issn.1000-4440.2019.05.002
文献标志码:
A
摘要:
为了解决常规的水稻种子发芽率测定方法存在的试验周期长且操作繁琐等问题，实现水稻种子发芽率的快速检测。本研究以黑龙江省五常市五优稻四号粳稻种子为研究对象，首先将7组种子样本(每组60个样本，共计420个样本)置于温度为45 ℃，湿度为90%的环境中分别进行为期0 d、1 d、2 d、3 d、4 d、5 d、6 d的不同时段的人工老化,然后采集每个水稻种子样本的光谱数据后进行发芽试验。对光谱数据使用蒙特卡洛交叉验证法进行异常样本剔除，并应用UVE法对全光谱数据进行特征波长选择，使光谱数据由全光谱的1 845个数据点缩减为524个数据点，最后建立PLSR预测模型。所建模型的预测集决定系数R2为0.817 0、RMSEP为2.183 0。试验结果表明，经UVE法降维后建立PLSR模型的各项参数均优于全光谱模型，因此，UVE特征波长选择算法为提高水稻种子发芽率测定模型的预测能力提供了一种新的途径。
Abstract:
In order to solve the problems of long test period and complicated operation of conventional methods for determining germination rate of rice seeds, a method of rapid detection of germination rate of rice seeds was put forward. In this study, the seeds of japonica rice Wuyou No.4 in Wuchang City of Heilongjiang province were taken as the research object. Firstly, seven groups of seed samples (60 samples in each group and 420 samples in total) were placed at a temperature of 45 ℃ and humidity of 90% for artificial aging at different time periods of 0 d, 1 d, 2 d, 3 d, 4 d, 5 d and 6 d, respectively. Secondly, the germination experiment was carried out after collecting the spectral data of each sample of rice seeds. Monte Carlo cross validation method was used to remove abnormal samples of spectral data, and UVE method was used to select the characteristic wavelength of the whole spectrum data. The spectral data were reduced from 184 5 data points to 524 data points. Finally, PLSR prediction model was established based on the 524 data points. The predictive set determination coefficient R2 of the model was 0.817 0 and RMSEP was 2.183 0. The results showed that the parameters of PLSR model after dimension reduction by UVE method were better than those of full spectrum model. Therefore, the UVE characteristic wavelength selection algorithm provides a new way to improve the prediction ability of rice seed germination rate measurement model.

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备注/Memo

备注/Memo:
收稿日期：2018-07-04 基金项目：国家重点研发计划项目(2016YFD0701300) 作者简介：曲歌(1994-),女，黑龙江大庆人，硕士研究生，主要从事近红外光谱的水稻种子品质分析研究。(E-mail)xqg1002@163.com 通讯作者：陈争光，(E-mail)ruzee@sina.com
更新日期/Last Update: 2019-11-11