INVERSION OF THE OPTICAL PROPERTIES OF APPLES BASED ON THE CONVOLUTIONAL NEURAL NETWORK AND TRANSFER LEARNING METHODS

An inversion of optical properties is an important test for determining the quality of fruit. The conventional inversion model of the optical properties uses measured hyperspectral images as the training data. Studies show that the conventional machine learning method for inverting the optical properties results in low inversion accuracy, especially with curved models. Hence, the present study uses a convolutional neural network scheme to train the simulated hyperspectral images. Moreover, the maximum mean discrepancy (MMD) transfer method is used to transfer the simulated hyperspectral images to the measured hyperspectral images of apples. To evaluate the performance of the proposed method, the present study uses it to classify a variety of an apple's optical properties, including the peel absorption, pulp absorption, peel scattering, and pulp scattering coefficients. The classification accuracies of the peel and pulp absorption coefficients are 84.61% and 92.47%, respectively. The classification accuracies of the peel and pulp scattering coefficients are 83.56% and 86.53%, respectively. These inversion results are compared with convolutional neural networks, neural networks, and sup-port vector machines with measured hyperspectral images. It was found that the proposed inversion model is an effective scheme for optical property inversion. To prove the necessity of optical property inversion, the least squares, decision tree and random forest regression methods are performed to analyze the correlation between the depth of optical characteristics and the brix and moisture. The present study shows that these correlations are in the form of 0.98 and 0.98. The correlation coefficients increase by 0.36 and 0.25 compared to the measured hyperspectral images. The conclusions show that the proposed inversion model is an effective scheme for apple optical property inversion.