Peach variety detection using VIS-NIR spectroscopy and deep learning.

• Peach variety detection using VIS-NIR spectroscopy. • Detection and classification without handcrafted feature extraction. • Best detection accuracy was 100% in the validation and 94.4% in the test. More than 1000 peach varieties with significant differences in qualities are cultivated in China. Distinguishing peach varieties is not only needed by peach dealers, but also demanded by products processing enterprises and consumers. Detecting the varieties of fruit is one of the most important analyses. Over the past decades, there are many linear or nonlinear methods have been proposed, such as principal component analysis, partial least squares, support vector machine etc. However, these traditional methods commonly need some preprocessing steps including denoising, baseline correction, wavelength selection and so on. Hence, it requires the users to have enough skilled knowledge before they can establish a good performance detection model. To offer information on identifying peach varieties, visual near-infrared (VIS-NIR) diffuse reflectance spectra between 350 and 820 nm were collected for five peach varieties, 100 samples for each variety, the total samples consisted of 500 peaches. Spectral information has shown the rapid non-destructive measurement ability in many research fields. In recent years, the spectral information was researched for detecting the sugar and firmness of fruits, for examples, apple, peach, tomato and so on. Some traditional detection methods using spectra require application specific transforms, expertly designed constraints and model parameters, and have limited detection performance due to their maintenance costs. With the development of machine learning technology in recent years, deep learning which plays an important role in different research projects has won the eyes of fields from both academy and industry. In order to classify peach varieties by analyzing VIS-NIR spectra, a detection method based on deep learning principle is proposed in this paper. In this paper, the method can realize multi-identification peach varieties, by constructing a one dimensional convolution neural network and establishing a VIS-NIR spectra database containing five kinds of peaches. The network model is obtained through training, and then is employed to predict the testing set data. The accuracy of models based on deep learning reached 100% in the validation dataset and 94.4% in the test dataset. This study indicated that peach varieties could be distinguished successfully by using VIS-NIR spectroscopy and deep learning. [ABSTRACT FROM AUTHOR]