基于中粒度数据融合与指纹区分析的赤霉病小麦 DON 级缺陷分类。
Mid-Level Data Fusion Combined with the Fingerprint Region for Classification DON Levels Defect of Fusarium Head Blight Wheat.
机构信息
College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China.
College of Engineering, Nanjing Agricultural University, Nanjing 210031, China.
出版信息
Sensors (Basel). 2023 Jul 22;23(14):6600. doi: 10.3390/s23146600.
In this study, a method of mid-level data fusion with the fingerprint region was proposed, which was combined with the characteristic wavelengths that contain fingerprint information in NIR and FT-MIR spectra to detect the DON level in FHB wheat during wheat processing. NIR and FT-MIR raw spectroscopy data on normal wheat and FHB wheat were obtained in the experiment. MSC was used for pretreatment, and characteristic wavelengths were extracted by CARS, MGS and XLW. The variables that can effectively reflect fingerprint information were retained to build the mid-level data fusion matrix. LS-SVM and PLS-DA were applied to investigate the performance of the single spectroscopic model, mid-level data fusion model and mid-level data fusion with fingerprint information model, respectively. The experimental results show that mid-level data fusion with a fingerprint information strategy based on fused NIR and FT-MIR spectra represents an effective method for the classification of DON levels in FHB wheat samples.
在本研究中，提出了一种基于中级别数据融合的方法，该方法结合了近红外和傅里叶变换中包含指纹信息的特征波长，以检测小麦加工过程中 FHB 小麦中的 DON 水平。实验中获得了正常小麦和 FHB 小麦的近红外和傅里叶变换中红外原始光谱数据。采用 MSC 进行预处理，并通过 CARS、MGS 和 XLW 提取特征波长。保留能够有效反映指纹信息的变量，构建中级别数据融合矩阵。应用 LS-SVM 和 PLS-DA 分别研究了单一光谱模型、中级别数据融合模型和中级别数据融合与指纹信息模型的性能。实验结果表明，基于融合近红外和傅里叶变换中红外光谱的中级别数据融合与指纹信息策略是一种有效的 FHB 小麦样本 DON 水平分类方法。
Zotero 插件