摘要
基于稻种老化时间不同时的物理学和生理学差异,提出一种基于红外热成像技术及广义回归神经网络的快速、无损检测稻种发芽率的检测方法,解决传统稻种发芽率检测方法操作复杂、实验周期长等问题。在温度为45℃、湿度为90%的条件下,将水稻种子依次老化0,1,2,3,4,5,6和7 d,得到不同发芽率的种子;采集稻种红外热图像,然后提取稻种胚芽部位数据,总计144份,随机分为校正集和预测集,其中校正集96份,预测集48份;分析和比较不同老化天数稻种红外热差异,从物理学和生理学方面揭示稻种发芽率与红外热图像间的关系,结合偏最小二乘算法(partial least squares,PLS)、BP(back propagation,BP)人工神经网络和广义回归神经网络(general regression neural network,GRNN),建立稻种发芽率的红外热模型。结果表明,利用GRNN建立的发芽率预测模型效果最优,其中校正集的Rc(相关系数)和SEC(标准偏差)分别为0.932 0和2.056 0,预测集RP(相关系数)和SEP(标准偏差)分别为0.900 3和4.101 2,相关性均达到较高水平且校正集与预测集的标准偏差均较小。实验结果表明,采用红外热成像技术结合广义回归神经网络研究稻种发芽率是可行的,且所建模型在稻种发芽率快速测定方面有较高的精度。
On the basis of the differences in physiology and physics of rice seed with different aging time,the paper proposes a fast and nondestructive method which is based on infrared thermal imaging technology and generalized regression neural network to detect the germination rate of rice seed.This method solves the problems of long experimental period,complex operations and other disadvantages of the traditional method which is used to detect germination rate.When the temperature is 45 ℃ and humidity is 90%,the rice seeds are aged for 0,1,2,3,4,5,6 and 7 d respectively to get rice seeds of different germination rate.The data of 144 groups was extracted from the germ of rice seed.This data was divided into two groups randomly：the calibration set was 96 groups and the prediction set was 48 groups.Through analyzing and comparing the differences of infrared thermal image of rice seeds of different aging days,the relationship in physics and physiology between germination rate of rice seed and infrared thermal images was revealed.The infrared prediction model for germination rate of rice seed was established by combining partial least squares algorithm,Back Propagationneural network and General regression neural network.The result shows that the optimal germination rate model is built with GRNN.In this model,the correlation coefficient（RC） and standard deviation（SEC） of calibration sets are 0.932 0 and 2.056 0.At the same time,the correlation coefficient（RP） and standard deviation（SEP） of prediction sets are 0.900 3 and 4.101 2.The relevance reaches a higher level and the standard deviation is small.Therefore,the experiment shows that combining infrared thermal imaging technology with GRNN to study germination rate of rice seed is feasible.The model has a higher accuracy in terms of rapid determination of the germination rate of rice seed.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2016年第8期2692-2697,共6页
Spectroscopy and Spectral Analysis
基金
The National Natural Science Foundation of China(31401610)
the Natural Science Foundation of Jiangsu Province(BK20130696)
the Fundamental Research Funds for the Central Universities(KYZ201427)
Remote Measurement and Control Technology Key Laboratory Open Fund of Jiangsu Province(YCCK201501)
关键词
红外热成像技术
稻种
发芽率
无损检测
GRNN
Infrared thermal imaging technology
Rice seed
Germination rate
Nondestructive detecting
GRNN
