Non-destructive detection method of apple quality based on hyperspectral and deep learning
The hyperspectral data of apples were obtained by using near-infrared hyperspectral imaging technology,and the indexes of sugar content and acidity were detected nondestructively.For the characteristics of large amount of hyper-spectral data and information redundancy,standardization(SS),standard normal variate(SNV),Savitzky-Golay smoot-hing filtering(SG)and multiplicative scatter correction(MSC)were used to preprocess the spectra of apples.According to the characteristic of hyperspectral images with many bands,successive projections algorithm(SPA),competitive adaptive reweighted sampling(CARS)algorithm and random frog(RF)algorithm were used to select the characteristic wavelengths of apples.Support vector machine(SVM)model,convolutional neural networks(CNN)model and quantitative spectral data analysis based on deep learning(DeepSpectra)model were used to predict the sugar-acid ratio of apples.The results showed that the prediction ac-curacy of DeepSpectra model was 93.70％,which had high accuracy and could be used to predict the sugar-acid ratio of apples.In this study,hyperspectral imaging technology and DeepSpectra model were combined to realize the non-destructive detection of the sugar-acid ratio of apples.
万方数据
维普
