【摘要】 激光背向散射成像是激光与生物组织相互作用产生散射光的成像，在农产品的品质分类中有着广泛的应用。利用激光背向散射成像技术与深度学习，实现马铃薯在不同存储情况下的品质分类。对激光背向散射进行理论分析，搭建激光背向散射成像的数据采集系统，对马铃薯样品进行激光背向散射图像采集，得到新鲜马铃薯、冰箱存储与室温存储马铃薯的激光背向散射成像数据集。对数据集利用改进后的VGG16网络进行训练，并与DenseNet121网络、原始VGG16网络的训练结果进行对比。结果显示，改进后的VGG16网络对数据集的分类准确率为95.33%。由此表明，激光背向散射成像结合深度学习可以实现马铃薯品质的智能分级。更多还原

【Abstract】 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 DenseNet121 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.更多还原