Optical-imaging detection of apple sugar content based on OpenCv and Pytorch

Abstract

Abstract Based on the Rayleigh scattering and Mie scattering, a scattering model in the apple pulp tissue is theoretically established, and the relationship between the sugar content of the apple pulp and the light intensity distribution of the diffuse reflection image is derived.Firstly, a homemade reflection-collection system has been applied to collect the scattering light intensity distribution of 133 apples with different varieties, skin colors, and sugar contents. According to acquired optical images 133 original images are processed based on OpenCv, and the appropriate region is extracted to recreate the image set, and the image features of the image set are derived with the aid of the denoising self-encoding neural network for data dimensionality reduction. The image feature and the recorded fructose content table are made into a data set, which is divided into a training set and a test set at a ratio of 3:1. Finally, through the training of the neural network, the non-destructive prediction of the apple sugar content is successfully achieved within a certain range.