摘要
目的应用高光谱成像技术,结合多模态融合方法,实现对玉米种子成熟度精准、无损检测。方法获取高、低成熟度玉米种子高光谱图像,采用自举软收缩算法与连续投影算法的级联算法(bootstrapping soft shrinkage-successive projections algorithm,BOSS-SPA)进行特征波长提取,采用灰度共生矩阵法(gray-level co-occurrence matrix,GLCM)进行图像纹理特征提取,选择能量、熵、相关性、逆方差和对比度5个特征参数,将光谱与图像数据进行特征级融合,利用偏最小二乘判别(partial least squares-discriminant analysis,PLS-DA)和最小二乘支持向量机(least squares support vector machine,LS-SVM)建立玉米种子成熟度分类模型。结果确定使用SG卷积平滑-标准正态变量变换(Savitzky-Golay convolution smoothing-standard normal variable,SG-SNV)作为最佳光谱预处理方法,采用BOSS-SPA方法提取的11个波长表现出良好建模性能,基于光谱图像融合数据的模型测试集总体识别准确率均达到95%以上。结论高光谱技术结合多模态特征融合方法有望成为玉米种子成熟度的无损检测提供切实可行的参考方法。
Objective To achieve accurate and non-destructive detection of Zea mays L.seed maturity by applying hyperspectral imaging technology combined with multimodal fusion methods.Methods Hyperspectral images of high and low maturity Zea mays L.seeds were acquired.The cascade algorithm of bootstrapping soft shrinkage and successive projections algorithm(BOSS-SPA)was used for feature wavelength extraction,while the gray-level co-occurrence matrix method(GLCM)was used for image texture feature extraction.Five feature parameters—energy,entropy,correlation,homogeneity and contrast were selected to integrate the spectra with the image data in a feature level fusion.Results The partial least squares-discriminant analysis(PLS-DA)and least squares support vector machine (LS-SVM) were used to establish a Zea mays L. seed maturity classification model. The use of Savitzky-Golay convolution smoothing-standard normal variable transformation (SG-SNV) was identified as the best spectral preprocessing method, and the 11 wavelengths extracted using the BOSS-SPA method showed good modelling performance, and the overall recognition accuracies of the model test set based on the fused data of the spectral images all reached over 95%. Conclusion Hyperspectral technology combined with multimodal feature fusion method is expected to provide a practical reference method for non-destructive detection of Zea mays L. seed maturity.
作者
曾柯宜
刘禹彤
张倩
陈媛媛
吴静珠
ZENG Ke-Yi;LIU Yu-Tong;ZHANG Qian;CHEN Yuan-Yuan;WU Jing-Zhu(Beijing Key Laboratory of Big Data Technology for Food Safety,Beijing Technology and Business University,Beijing 100048,China)
出处
《食品安全质量检测学报》
2025年第2期171-177,共7页
Journal of Food Safety and Quality
基金
国家重点研发计划项目(2018YFD0101004-03)
国家自然科学基金项目(61807001)。
关键词
高光谱成像
玉米种子成熟度
多模态融合
特征波长提取
纹理特征提取
hyperspectral imaging
Zea mays L.seed maturity
multimodal fusion
characteristic wavelength extraction
texture feature extraction
