基于GASF变换和深度学习的柑橘内部品质分析  

Citrus internal quality analysis based on GASF transformation and deep learning

作　　者：陈浩宇 苗玉彬[1] Chen Haoyu;Miao Yubin(School of Mechanical and Power Engineering,Shanghai Jiao Tong University,Shanghai,200240,China)

机构地区：[1]上海交通大学机械与动力工程学院,上海市200240

出　　处：《中国农机化学报》2025年第4期133-138,162,共7页Journal of Chinese Agricultural Mechanization

基　　金：国家自然科学基金项目(51975361)。

摘　　要：针对现有柑橘内部品质无损检测模型存在的光谱信息丢失、检测精度不高等问题,提出一种基于格拉姆角和场(GASF)变换和深度学习的内部品质无损定性分析方法。通过GASF变换将采集柑橘的可见—近红外慢透射一维光谱数据转换为二维图像,将移动平均平滑(MA)、标准正态变换(SNV)等预处理方法作为数据增强方法实现数据扩充。设计二维卷积神经网络(2D—CNN)模型并加入卷积注意力机制模块(CBAM)以提高模型对GASF图像的特征提取能力。结果表明,与传统机器学习模型支持向量机(SVM)、随机森林(RF)相比,神经网络模型对光谱信息提取能力更强,预测准确率更高。SVM和RF预测准确率分别为84.85%和81.82%,2D—CNN预测准确率为87.88%,加入CBAM后预测准确率提高至93.94%。GASF变换可将神经网络在图像处理中的优势引入可见—近红外光谱分析中,为水果内部品质无损检测提供新思路和理论参考。Aiming at the problems of spectral information loss and low detection accuracy in the current citrus fruit internal quality inspection model,a new method based on Gramian Angular Summation Field(GASF)transformation and deep learning was proposed for non-destructive qualitative analysis of internal citrus fruit quality.Through GASF transformation,the collected one-dimensional spectra data in the visible-near infrared range of citrus fruits are converted into two-dimensional images.The data are augmented by Moving Average(MA),Standard Normal Variate(SNV)and others preprocessing methods.A two-dimensional Convolutional Neural Network(2D—CNN)model is designed,incorporating a Convolutional Block Attention Module(CBAM)to enhance the model's feature extraction capabilities from GASF images.Experimental results reveal that,compared to traditional machine learning models such as Support Vector Machine(SVM)and Random Forest(RF),the neural network model exhibits stronger spectral information extraction capability and higher predictive accuracy.The prediction accuracy of SVM and RF are 84.85%and 81.82%respectively.The predictive accuracy of 2D—CNN is reported as 87.88%,which increases to 93.94%with the incorporation of CBAM.The study indicates that GASF transformation effectively introduces the advantages of neural networks in image processing into visible-near infrared spectral analysis,offering new insights and theoretical references for non-destructive internal fruit quality inspection.

关 键 词：柑橘 格拉姆角和场 深度学习 可见—近红外光谱 卷积注意力机制 

分 类 号：S126[农业科学—农业基础科学] O657.3[理学—分析化学]