Generation of fruit’s spectra with hundreds of wavelengths from obtained multi-spectra and spectral application using deep learning

Abstract

Although multi-spectral technology has been widely used to develop low-cost and low-power fruit internal quality detector, the detection precision was poor because of few wavelengths used. To improve the detection precision, we proposed a novel generation method on fruit’s false spectra with hundreds of wavelengths (named false full-spectra) from obtained multi-spectra with 12 wavelengths for kiwifruit and 11 wavelengths for pear using deep convolutional generative adversarial network (DCGAN), and the method was evaluated by traditional partial least squares regression (PLSR) and one dimensional convolutional neural network models (1D-CNN). The results showed that the generated false full-spectra obtained by DCGAN had high similarity with the ‘real’ full-spectra no matter for kiwifruit and pear. The mean structural similarity between the false full-spectra and the ‘real’ full-spectra of prediction set of kiwifruit and pear was 0.9490 and 0.9877, respectively, and their mean peak signal-to-noise ratio was 35.82 dB and 44.59 dB, respectively. The prediction performances of the built PLSR and 1D-CNN models based on false full-spectra for internal qualities (soluble solids content and firmness) of kiwifruit and pear were much better than those based on multi-spectra and were very close to those based on ‘real’ full-spectra. This study provides a novel idea to improve the detection performance of the multi-spectra-based detector without increasing the hardware cost.