森林工程2025，Vol.41Issue(3)：603-613,11.DOI:10.7525/j.issn.1006-8023.2025.03.017
基于高光谱图谱融合的蓝莓可溶性固形物含量检测
Detection of Soluble Solids Content in Blueberries Based on Hyperspectral Image and Spectrum Fusion
摘要
Abstract
Soluble solids content(SSC)is a key indicator for assessing the internal quality of fruits.This study proposes a non-destructive detection method based on hyperspectral image fusion to predict the SSC of blueberries.Three widely used wavelength dimensionality reduction algorithms are employed:Monte Carlo uninformative variable elimination(MC-UVE),Competitive Adaptive Reweighted Sampling(CARS),and Successive Projections Algorithm(SPA),to identify optimal wavelengths.Additionally,a strategy integrating Local Binary Patterns(LBP)and Gray Level Co-occurrence Matrix(GLCM)is proposed for feature extraction.Using spectral features,image features,and fused features,Partial Least Squares(PLS),Backpropagation Neural Network(BPNN),and Support Vector Machine(SVM)models are de-veloped for SSC prediction.The results demonstrate that the BPNN model,utilizing spectral features extracted via the CARS algorithm and image features derived from the LBP+GLCM algorithm,yields the highest prediction accuracy.The model's coefficient of determination(R2p)is 0.926 1,while the Root Mean Square Error of Prediction(RMSEP)is 0.364 1.This study indicates that hyperspectral image fusion technology holds significant potential for the non-destruc-tive prediction of blueberry SSC.关键词
可溶性固形物含量/无损检测/信息融合/特征提取/机器学习Key words
Soluble solid content/non-destructive assessment/information fusion/feature extraction/machine learning分类
轻工纺织引用本文复制引用
孙枭雄,刘大洋,朱良宽..基于高光谱图谱融合的蓝莓可溶性固形物含量检测[J].森林工程,2025,41(3):603-613,11.基金项目
国家自然科学基金项目(32202147) （32202147）
黑龙江省博士后科研基金项目(LBH-Q13007). （LBH-Q13007）
