基于特征光谱和GRNN 的糙米发芽率快速检测方法研究

李欢欢; 卢伟; 洪德林; 党晓景; 梁琨

doi:doi:10.3788/lop52.113005

激光与光电子学进展, 2015, 52 (11): 113005, 网络出版: 2015-11-09

基于特征光谱和GRNN 的糙米发芽率快速检测方法研究下载： 506次

Rapid Testing Method of Brown Rice Germination Rate Based on Characteristic Spectrum and General Regression Neural Network

论文大纲

李欢欢 ^1,*卢伟 ^1,2洪德林 ³党晓景 ³梁琨 ¹

作者单位

¹ 南京农业大学工学院江苏省现代设施农业技术与装备工程实验室, 江苏南京 210031

² 远程测控技术江苏省重点实验室, 江苏南京 210096

³ 南京农业大学农学院作物遗传与种质创新国家重点实验室, 江苏南京 210095

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摘要

针对种子发芽率检测常用方法操作复杂、周期长、受种子休眠期影响等问题，提出了一种基于特征光谱和广义回归神经网络(GRNN)的糙米发芽率快速检测方法。在温度为45 ℃、湿度为90%的条件下，对稻种进行高温高湿人工老化，老化时间为0、24、48、72、96、120、144、168 h；人工去壳处理后，采集近红外光谱数据，将160 份糙米样品的光谱分为校正集(120 份)和预测集(40 份)；采用标准正态变换(SNV)、一阶导数(FD)对光谱数据进行预处理，提取特征波长，分析不同建模方法和不同贡献率的特征波长对模型的影响。结果表明，以688、1146、1346、1366、1396、1686 nm对应的光谱作为输入，通过GRNN 建立的模型最优，其校正集相关系数(R_C)与标准偏差(SEC)分别为0.9743、1.9161，预测集相关系数(R_P)与标准偏差(SEP)分别为0.9505、2.3423。研究表明，采用近红外光谱分析技术对糙米发芽率进行检测是可行的，能够从稻种生理学特性的角度揭示不同发芽率稻种的光谱差异，且所建模型在水稻发芽率预测方面有较好的预测能力，为便携式水稻发芽率光谱仪的研制提供了理论依据。

Abstract

Considering that the current methods of germination rate detection are complex, time-consuming and affected by seed dormancy, a method for rapid detection of brown rice germination rate based on characteristic spectrum and general regression neural network (GRNN) is proposed. Under the condition of temperature 45 ℃ and relative humidity 90%, rice seeds are aged artificially for 0, 24, 48, 72, 96, 120, 144, 168 h. Spectral data of 160 samples are collected by a near-infrared spectrometer after artificial shelled processing and divided into a calibration set (120 samples) and a prediction set (40 samples). Characteristic wavelengths are extracted after standard normalized variate (SNV) and first derivative (FD) preprocessing. The impact of different modeling methods and characteristic wavelengths on the model is analyzed. The experimental results show that the optimal model is constructed by GRNN with the spectral data of 688, 1146, 1346, 1366, 1396, 1686 nm. The correlation coefficients of the calibration set (R_C) and the prediction set (R_P) are 0.9743 and 0.9505, and the standard errors of the calibration set (SEC) and the prediction set (SEP) are 1.9161 and 2.3423. The research results show that it is feasible to measure the germination rate of brown rice seeds by using near infrared spectroscopy. The model has better predictive ability in germination rate, and reveals the difference between rice seeds with different germination rate from the perspective of physiological characteristics. This method provides a theoretical basis for the development of portable spectrometer for rice seed germination rate detection.

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李欢欢, 卢伟, 洪德林, 党晓景, 梁琨. 基于特征光谱和GRNN 的糙米发芽率快速检测方法研究[J]. 激光与光电子学进展, 2015, 52(11): 113005. Li Huanhuan, Lu Wei, Hong Delin, Dang Xiaojing, Liang Kun. Rapid Testing Method of Brown Rice Germination Rate Based on Characteristic Spectrum and General Regression Neural Network[J]. Laser & Optoelectronics Progress, 2015, 52(11): 113005.

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