利用激光背向散射成像的马铃薯品质智能分级

Laser backscattering imaging is the imaging of scattered light generated by the interaction between laser and biological tissue. It is widely used in the quality classification of agricultural products. By using laser backscatter imaging and deep learning, potato quality classification under different storage conditions was realized. The laser backscattering imaging data collection system was established based on the theoretical analysis of laser backscattering imaging. The laser backscattering image collection was carried out on potato samples, and the laser backscattering imaging data sets of fresh potatoes, refrigerator storage and room temperature storage potatoes were obtained. The data set is trained using the improved VGG16 network, and the training results are compared with the DenseNetl21 network and the original VGG16 network. The results show that the classification accuracy of the improved VGG16 network is 95. 33%. The results show that the combination of laser backscatter imaging and deep learning can achieve intelligent classification of potato quality.