CAJ | 학술논문

对大豆进行快速准确分级，采集1~5等级大豆波长在1000~2500 nm范围的高光谱图像数据，获得光谱图像；对不同大豆等级样本的光谱曲线进行分析；通过主成分分析法，从每个等级大豆样本中优选出四个特征波长，得到特征图像；从每个特征图像中分别提取基于灰度共生矩阵的4个纹理特征参数--能量、熵、惯性矩和相关性，从16个特征变量中选取8个主要特征变量，应用BP神经网络建立大豆品质分级识别模型。模型预测准确率为92%。结果表明，高光谱图像技术对大豆等级具有较好的识别作用，可为大豆的在线无损检测分级提供参考。
대대두진행쾌속준학분급，채집1~5등급대두파장재1000~2500 nm범위적고광보도상수거，획득광보도상；대불동대두등급양본적광보곡선진행분석；통과주성분분석법，종매개등급대두양본중우선출사개특정파장，득도특정도상；종매개특정도상중분별제취기우회도공생구진적4개문리특정삼수--능량、적、관성구화상관성，종16개특정변량중선취8개주요특정변량，응용BP신경망락건립대두품질분급식별모형。모형예측준학솔위92%。결과표명，고광보도상기술대대두등급구유교호적식별작용，가위대두적재선무손검측분급제공삼고。
In order to fast and exact classification of soybean,col ection 1-5 grades soybean 1 000-2 500 nm range of hyperspectral image data to obtain spectral image; analysis of different samples of soybean grade spectral curve;application of principal component analysis (PCA), from the 4 features of each variety selected optimal wavelength, extracted four texture feature parameters(moment of inertia, energy, entropy and correlation)from each feature in the image based on statistical moment. Select 8 main characteristic variables from 16 characteristic variables, establishment of soybean grade identification model based on BP neural network. Experimental results showed that discriminating rate was 92%in the prediction set. Results showed that the hyperspectral image technology had better recognition effects on soybean grade, Provided a good reference for soybean online non-destructive testing classification.