Abstract
Accurate and nondestructive maturity discrimination of tomatoes is significant for harvest and preservation. Color differences between intermediate adjacent maturity stages are not significant, so it is time-consuming to obtain a large amount of precise maturity labels and difficult to discriminate multiple maturity stages by visual features merely. This study designed a semi-supervised method based on hyperspectral imaging technology for tomato maturity discrimination by using a small amount of labeled samples. Firstly, the hyperspectral data of tomato samples was extracted by the hyperspectral imaging system and pre-processed by the multiple scattering correction algorithm. Then, the class probability information of unlabeled samples was described by the sparse coding of labeled samples. Next, a semi-supervised algorithm based on Laplacian score and spectral information divergence (SIDLS), which used class probability information to construct graphs, was designed to select a representative waveband subset. Finally, the sparse representation model based on class probability information (CSR) was established to construct a connection graph, and label propagation algorithm was used to discriminate tomato maturity. Experimental results demonstrated that SIDLS algorithm had an advantage in the feature selection over semi-supervised algorithm based on Laplacian score (SSLS) and Semi_Fisher Score (SFS) algorithm, and CSR model was also superior to other graph construction methods in constructing a more discriminative graph. The discrimination accuracy of this method could reach 96.78%, which shows a promising prospect in tomato maturity discrimination.
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