Abstract
Soluble solids content (SSC) is one of the important components for evaluating fruit quality. The rapid development of hyperspectral imagery provides an efficient method for non-destructive detection of SSC. Previous studies have shown that the internal quality evaluation of fruits based on spectral information features achieves better results. However, the lack of comprehensive features limits the accurate estimation of fruit quality. Therefore, the deep learning theory is applied to the estimation of the soluble solid content of peaches, a method for estimating the SSC of fresh peaches based on the deep features of the hyperspectral image fusion information is proposed, and the estimation models of different neural network structures are designed based on the stack autoencoder–random forest (SAE-RF). The results show that the accuracy of the model based on the deep features of the fusion information of hyperspectral imagery is higher than that of the model based on spectral features or image features alone. In addition, the SAE-RF model based on the 1237-650-310-130 network structure has the best prediction effect (R2 = 0.9184, RMSE = 0.6693). Our research shows that the proposed method can improve the estimation accuracy of the soluble solid content of fresh peaches, which provides a theoretical basis for the non-destructive detection of other components of fresh peaches.
Highlights
Peach is a kind of fruit loved by different consumers because of its high nutrition and unique taste and flavor
The soluble solid content indicator bar below the graph shows the range of soluble solid content the original hyperspectral images of fresh peaches and the second row represents the corresponding in fresh peaches of different maturity
Deep learning theory is applied to the estimation of the soluble solid content of peaches, and the stacked autoencoder (SAE) feature extraction of unsupervised training is combined with the fine-tuning of supervised training
Summary
Peach is a kind of fruit loved by different consumers because of its high nutrition and unique taste and flavor. Multispectral [4,5], fluorophore [6,7,8], near-infrared spectroscopy [9,10,11,12], electronic nose [13,14,15], and dielectric technology [16,17] have been applied for the evaluation of the soluble solid content in fresh fruits Among these technologies, near-infrared spectroscopy is currently the most widely used method for evaluating fresh fruit SSC due to its fast, simple, and non-destructive characteristics. Lacking the spatial characteristics of fruit hyperspectral images limits the further exploration of fruit SSC prediction models
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