基于高光谱成像技术的山楂含水量快速无损分析方法
Rapid and Non-Destructive Analysis Method of Hawthorn Moisture Content Based on Hyperspectral Imaging Technology
摘要
Abstract
[Objective]This study aimed to develop a rapid and non-destructive method for determining the moisture content of haw-thorn fruits using hyperspectral imaging(HSI)integrated with machine learning algorithms.By evaluating the effects of different fruit orientations and spectral ranges,the research provides theoretical insights and technical support for real-time moisture monitoring and intelligent fruit sorting.[Methods]A total of 458 fresh hawthorn samples,representing various regions and cultivars,were col-lected to ensure diversity and robustness.Hyperspectral images were acquired in two spectral ranges:visible-near-infrared(VNIR,400～1 000 nm)and short-wave infrared(SWIR,940～2 500 nm).A threshold segmentation algorithm was used to extract the region of interest(ROI)from each image,and the average reflectance spectrum of the ROI served as the raw input data.To enhance spectral quality and reduce noise,five preprocessing techniques were applied:Savitzky-Golay(SG)smoothing,multiplicative scatter correc-tion(MSC),standard normal variate(SNV),first derivative(FD),and second derivative(SD).Four regression algorithms were then employed to build predictive models:partial least squares regression(PLSR),support vector regression(SVR),random forest(RF),and multilayer perceptron(MLP).The models were evaluated under varying fruit orientations(stem-side facing downward,upward,sideways,and a combined set of all three)and spectral ranges(VNIR,SWIR,and VNIR+SWIR).To further reduce the dimensionality of the hyperspectral data and minimize redundancy,four feature selection methods were applied:successive projections algorithm(SPA),competitive adaptive reweighted sampling(CARS),variable iterative space shrinkage approach(VISSA),and discrete wavelet transform combined with stepwise regression(DWT-SR).The DWT-SR method utilized the Daubechies 6(db6)wavelet basis func-tion at a decomposition level of 1.[Results and Discussions]Both fruit orientation and spectral range had a significant impact on model performance.The optimal prediction results were achieved when the stem-side of the fruit was facing downward,using the SWIR range(940～2 500 nm)and FD preprocessing.Under these conditions,the SVR model exhibited the highest predictive accuracy,with a coefficient of determination(R2ₚ)of 0.860 5,mean absolute error(MAEₚ)of 0.711 1,root mean square error(RMSEₚ)of 0.914 2,and residual prediction deviation(RPD)of 2.677 6.Further feature reduction using the DWT-SR method resulted in the selection of 17 key wavelengths.Despite the reduced input size,the SVR model based on these features maintained strong predictive capability,achieving R2ₚ=0.857 1,MAEₚ=0.669 2,RMSEₚ=0.925 2,and RPD=2.645 7.These findings confirm that the DWT-SR method ef-fectively balances dimensionality reduction with model performance.The results demonstrate that the SWIR range contains more moisture-relevant spectral information than the VNIR range,and that first derivative preprocessing significantly improves the correla-tion between spectral features and moisture content.The SVR model proved particularly well-suited for handling nonlinear relation-ships in small datasets.Additionally,the DWT-SR method efficiently reduced data dimensionality while preserving key information,making it highly applicable for real-time industrial use.[Conclusions]In conclusion,hyperspectral imaging combined with appropriate preprocessing,feature selection,and machine learning techniques offers a promising and accurate approach for non-destructive mois-ture determination in hawthorn fruits.This method provides a valuable reference for quality control,moisture monitoring,and auto-mated fruit sorting in the agricultural and food processing industries.关键词
山楂/高光谱成像/小波变换/支持向量机/含水量/机器学习Key words
hawthorn/hyperspectral imaging/wavelet transform/support vector machine/moisture content/machine learning分类
信息技术与安全科学引用本文复制引用
白瑞斌,王慧,王宏鹏,洪家顺,周骏辉,杨健..基于高光谱成像技术的山楂含水量快速无损分析方法[J].智慧农业（中英文）,2025,7(4):95-107,13.基金项目
国家重点研发计划项目(2024YFC3506800) （2024YFC3506800）
中国中医科学院科技创新工程项目(CI2023E002) （CI2023E002）
中央本级重大增减支项目(2060302) （2060302）
国家中医药管理局高水平中医药重点学科建设项目(ZYYZDXK-2023244) （ZYYZDXK-2023244）
财政部和农业农村部国家现代农业产业技术体系项目(CARS-21) （CARS-21）
中国中医科学院基本科研业务费优秀青年科技人才培养专项(ZZ16-YQ-040) （ZZ16-YQ-040）
中国中医科学院中药资源中心自主选题研究项目(ZZXT202312) National Key R&D Program of China(2024YFC3506800) （ZZXT202312）
Scientific and Technological Innovation Project of Chi-na Academy of Chinese Medical Sciences(CI2023E002) （CI2023E002）
Major Increase and Decrease in Expenditure at The Central Level(2060302) （2060302）
National Administration of Traditional Chinese Medicine High-level Key Discipline Construction Project of Traditional Chinese Medicine(ZYYZDXK-2023244) （ZYYZDXK-2023244）
China Agricultural Research System of MOF and MARA(CARS-21) （CARS-21）
Excellent Young Scientists Cultivation Program of China Academy of Chinese Medical Sciences(ZZ16-YQ-040) （ZZ16-YQ-040）
Independent Research Project of Na-tional Resource Center for Chinese Materia Medica,China Academy of Chinese Medical Sciences(ZZXT202312) （ZZXT202312）
