食品与机械2025，Vol.41Issue(6)：136-143,8.DOI:10.13652/j.spjx.1003.5788.2025.60040
融合改进深度卷积神经网络和光谱技术的猕猴桃内部品质无损检测
Non-destructive detection of kiwifruit internal quality based on improved deep convolutional neural network and spectral technology
摘要
Abstract
[Objective]Dry matter and sugar content are two important indicators affecting the quality of kiwifruit.To achieve rapid and accurate detection of these indicators,a non-destructive detection method for key internal quality indicators of kiwifruit is proposed,integrating improved deep convolutional neural network with spectral technology.[Methods]A spectrometer was used to collect spectral data of kiwifruit,and the data were transformed into two types of two-dimensional images using Gramian Angular Field(GAF)transformation.An improved convolutional neural network model with multi-dilated convolutions was constructed to predict and analyze key quality indicators of kiwifruit.The model consists of two independent CNN modules connected in parallel to process the two types of two-dimensional images.Multi-dilated convolution strategies,clustering pruning methods,and channel attention mechanisms were incorporated to enhance the model's detection and analysis performance.[Results]Compared with other models,the proposed method reduced the average root mean square errors of dry matter and sugar content by 20.59%and 13.04%,respectively,increased the average determination coefficients by 6.45%and 4.34%,respectively,and improved the average relative prediction deviations by 6.99%and 12.78%,respectively.[Conclusion]The proposed method demonstrates good capability in detecting and analyzing key internal quality indicators of kiwifruit,and provides a valuable reference for non-destructive internal quality testing of kiwifruit.关键词
猕猴桃/光谱技术/卷积神经网络/干物质/糖度Key words
kiwifruit/spectral technology/convolutional neural network/dry matter/sugar content引用本文复制引用
陈玮,费文源,栗超,魏晨曦..融合改进深度卷积神经网络和光谱技术的猕猴桃内部品质无损检测[J].食品与机械,2025,41(6):136-143,8.基金项目
教育部高等学校科学研究发展中心专项课题(编号:ZJXF20236031) （编号:ZJXF20236031）
