Maturity Detection of Camellia oleifera by Hyperspectral Imaging

In order to solve the problem of the reduction in the yield of camellia oil caused by inaccurate judgment of the picking date of Camellia oleifera, hyperspectral imaging technology combined with chemometrics was used to qualitatively classify the maturity of C. oleifera. The changes in the physicochemical and spectral characteristics of C. oleifera fruit at different maturity stages were examined. Curvature correction of hyperspectral images was carried out. Four classification algorithms were used to establish a discriminant model for C. oleifera fruit maturity based on the full-band spectral data, and it was found that the support vector machine (SVM) model had the highest classification accuracy (97%). Five feature variable selection methods were used to reduce the dimension of the full-band spectral data, and it was found that the accuracy of the model based on the feature wavelengths selected by competitive adaptive reweighted sampling (CARS) was the highest (82%). The accuracy of the SVM model based on a combination of the color and texture features extracted from the hyperspectral images was higher than that of the model based on single spectral features (whose dimension was reduced by CARS), with classification accuracy for the training and test sets of 95% and 93%, respectively. In conclusion, hyperspectral imaging technology can be used to classify C. oleifera with different maturities, which will provide a scientific basis for the judgment of the best picking date of C. oleifera, and is of great significance in ensuring the maximum tea seed yield and the optimum oil quality.