摘要
稻谷在储藏和运输过程中,在适宜的温湿度环境下极易发生霉变,导致大量的粮食浪费和巨大的经济损失,进而影响粮食安全。为解决传统的稻谷霉变检测存在的繁琐且耗时较长等不足,提出了基于近红外光谱图像处理和神经网络的稻谷霉变程度检测方法。首先,通过农业多光谱相机(Sequoia)和固定光源等设备,构建了霉变稻谷近红外图像数据采集平台,获取了黑龙江地区牡响、早香、彩稻三个品种不同的霉变状态(健康稻谷、轻度霉变、中度霉变)的近红外光谱成像数据。对于近红外光谱图像的160×160像素有效区域,应用数字图像处理技术结合光谱图像分析方法,分析了NIR图像多种纹理特征和光谱反射值频率特性,优选不同稻谷品种霉变状态的光谱特征,计算了近红外图像的纹理特征(均值、标准差、平滑度、三阶距、一致性、信息熵、平均梯度、分形维数)以及间隔步长0.1时NIR光谱图像在0.2~0.8区间反射值频率,共计14维度的光谱图像特性指标。最后,以提取NIR图像的特征向量为依据,利用前馈神经网络的自适应推理机制,建立了稻谷霉变程度与其近红外图像特征之间的非线性映射模型,该神经网络结构为14-60-3型,进一步将网络输出编码向量解析至稻谷霉变等级,实现了稻谷霉变程度的快速检测方法。结果表明:本文提出检测模型在学习次数为28455次时达到预设的目标精度0.06,所提取的稻谷NIR图像特征与模型输出的相关系数为0.85。仿真测试中该检测模型所计算出的网络输出值和期望输出值的误差平均值为0.52139,方差为0.13782,误差的标准差为0.37123,对于不同稻谷的霉变程度检测的准确率为93.33%。该研究为实现稻谷霉变程度无损检测提供了新方法,为稻谷仓储时霉变早期自动快速检测提供了技术支持。
During the storage and transportation of rice,mildew easily occurs in a suitable temperature and humidity environment will cause a lot of food waste and huge economic losses,which in turn affects food security.This paper proposed a method for detecting the mildew degree of rice-based on near-infrared spectroscopy image processing technology and neural network.First of all,through the agricultural multi-spectral cameras(Sequoia)and fixed light sources and other equipment,this research has constructed a near-infrared image data acquisition platform for moldy rice.The imaging data of the different mold states(three states:healthy rice,mild mold,and moderate mold)of three varieties of Muxiang,Zaoxiang,and Caidao in Heilongjiang area were acquired.Secondly,taking data samples of rice with different degrees of mildew as the research object,for the 160×160 pixel effective area of the infrared spectrum(NIR)image,applying digital image processing technology combined with spectral image analysis methods to study the various texture characteristics and spectral reflectance frequency characteristics of near infrared spectroscopy(NIR)images,optimizing the spectral characteristics of the mildew state of different rice varieties.The texture features(mean,standard deviation,smoothness,third-order distance,consistency,information entropy,average gradient,fractal dimension)of the near-infrared image are extracted,and the reflection value frequency of the NIR spectrum in the 0.2~0.8 interval when the interval step is 0.1,based on a total of 14-dimensional spectral image characteristic index.At last,based on the feature vector of the NIR image,using the feedforward neural network adaptive inference mechanism,a nonlinear mapping model between the degree of rice mildew and its near-infrared image characteristics was established.The network structure of the model is 14-60-3,and the network output code vector is analyze to the rice mildew grade,realizing the rapid detection method of rice mildew degree.The results show that this paper proposes that the detection model reaches the preset target accuracy of 0.06 when the number of learning times is 28455,and the correlation coefficient between the extracted rice NIR image features and the model output is 0.85.In the simulation test,the average error between the network output value calculated by the detection model and the expected output value is 0.52139,the variance is 0.13782,and the standard deviation of the error is 0.37123.The accuracy of detecting the degree of mildew of different rice is 93.33%.The research results are a new method for realizing the non-destructive detection of the degree of rice mildew and can provide technical support early and automatic and rapid detection of early mildew during rice storage.
作者
温冯睿
关海鸥
马晓丹
左锋
钱丽丽
WEN Feng-rui;GUAN Hai-ou;MA Xiao-dan;ZUO Feng;QIAN Li-li(College of Information and Electrical Engineering,Heilongjiang Bayi Agricultural University,Daqing 163000,China;College of Food Science,Heilongjiang Bayi Agricultural University,Daqing 163000,China;National Coarse Cereals Engineering Research Center,Daqing 163000,China;Key Laboratory of Agro-Products Processing and Quality Safety of Heilongjiang Province,Daqing 163000,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2022年第2期428-433,共6页
Spectroscopy and Spectral Analysis
基金
黑龙江省自然科学基金项目(LH2021C062)
国家自然科学基金项目(31601220)
国家重点研发计划项目(2018YFD0401403)
黑龙江八一农垦大学三横三纵支持计划项目(TDJH202101和ZRCQC202006)资助。
作者简介
温冯睿,1997年生,黑龙江八一农垦大学信息与电气工程学院硕士研究生,e-mail:wfr_369@163.com;通讯作者:关海鸥,e-mail:gho123@163.com;通讯作者:左锋,e-mail:zuofeng-518@126.com。
