基于高光譜圖譜融合的藍(lán)莓可溶性固形物含量檢測(cè)
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關(guān)鍵詞：可溶性固形物含量；無(wú)損檢測(cè)；信息融合；特征提??；機(jī)器學(xué)習(xí) 中圖分類號(hào)：TS255.7；O439；TP183 文獻(xiàn)標(biāo)識(shí)碼：A DOI： 10.7525/j.issn.1006-8023.2025.03.017
Abstract： Soluble solids content （SSC）is akey indicatorfor assessing the internal qualityoffruits.This study proposes anon-destructive detection method based on hyperspectral image fusion to predict the SSCof blueberries.Three widely used wavelength dimensionalityreduction algorithms areemployed：Monte Carlo uninformative variable elimination（MCUVE），Competitive Adaptive Reweighted Sampling（CARS），and Successive Projections Algorithm（SPA），，to identify optimal wavelengths.Additionally，astrategy integrating Local Binary Paterns（LBP）and GrayLevel 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 developed for SSC prediction.Theresults demonstrate that the BPNN model，utilizing spectral features extractedvia the CARS algorithm and image featuresderived from the LBP+GLCM algorithm，yields the highest prediction accuracy.The model'scoefficient of determination（ ）isO.9261，while the Root Mean Square Error ofPrediction（RMSEP）is 0.3641.Thisstudyindicates that hyperspectral image fusion technology holds significant potential forthenon-destructiveprediction of blueberry SSC.
Keywords：Soluble solidcontent；non-destructive assessment； information fusion；feature extraction;machine learning
0 引言
藍(lán)莓作為重要的林下經(jīng)濟(jì)作物，因其獨(dú)特的風(fēng)味和豐富的營(yíng)養(yǎng)，深受消費(fèi)者喜歡[1-2]。（剩余14781字）