摘要
【目的】针对目前玉米种子发芽率快速无损检测方法易受种子表皮颜色影响的问题,拟采用光声光谱深度扫描技术提高玉米种子发芽率的检测精度。【方法】选取3种不同颜色、6个品种的玉米样本,利用人工老化方法得到8种不同老化时间的玉米种子;通过调制光谱频率获得7种不同深度的光声光谱信息,并利用主成分分析分别得到最佳扫描频率和特征光谱,比较偏最小二乘法回归、BP神经网络、广义回归神经网络和支持向量回归等发芽率预测模型精度。【结果】光声光谱最佳扫描频率为500 Hz,支持向量回归的预测模型精度最高,相关系数均超过0.9800。6个品种玉米种子的发芽率预测相关系数分别为0.9838,0.9847,0.9836,0.9878,0.9833和0.9947,6个品种混合的玉米种子发芽率预测相关系数为0.9421。【结论】通过拓展光谱、声音和深度信息,光声光谱深度扫描技术适用于不同颜色的玉米发芽率高精度检测,具有较好的泛化能力。
【Objective】In view of the problem that the existed rapid and non-destructive maize seed germination rate testing methods are easily affected by the color of seed skin,the photoacoustic spectroscopy deep scanning technology was proposed to improve the detection accuracy of maize seed germination rate.【Method】Six maize cultivar seeds with three different colors were selected and treated using artificial aging method to obtain eight kinds of maize seeds with different aging time.The photoacoustic spectrum information with seven different depths was obtained by modulating the spectral frequency.The best scanning frequency and characteristic spectrum were determined by principal component analysis method.Different modeling approaches including partial least squares regression,back propagation neural network,generalized regression neural network and support vector regression were applied for comparing the prediction accuracy to optimize maize seed germination rate model.【Result】The best scanning frequency of photoacoustic spectrum was 500 Hz.The prediction model accuracy of support vector regression was the highest,and the correlation coefficients were all over 0.9800.The prediction correlation coefficients of germination rates of six maize cultivar seeds were 0.9838,0.9847,0.9836,0.9878,0.9833 and 0.9947 respectively,while that of the mixed six cultivar maize seeds reached 0.9421.【Conclusion】Through expanding the spectrum,sound and depth information,the photoacoustic spectrum depth scanning technology has a good generalization ability,and is suitable for high-precision germination rate detection of maize with different colors.
作者
王新宇
牛鹏帅
卢伟
DENG Yiming
WANG Xinyu;NIU Pengshuai;LU Wei;DENG Yiming(College of Artificial Intelligence,Nanjing Agricultural University/Jiangsu Provincial Laboratory of Modern Facility Agriculture Technology and Equipment Engineering,Nanjing 210031,China;College of Engineering,Michigan State University,East Lansing 48824,USA)
出处
《华南农业大学学报》
CAS
CSCD
北大核心
2020年第6期119-125,共7页
Journal of South China Agricultural University
基金
国家自然科学基金(32071896,31960487)
江苏省自然科学基金面上项目(BK20181315)
江苏省农业科技自主创新资金[CX(20)3068]
扬州市重点研发计划(现代农业)(YZ2018038)。
关键词
光谱学
光声光谱
玉米种子
发芽率
支持向量回归
spectroscopy
photoacoustic spectroscopy
maize seed
germination rate
support vector regression
