基于可见/近红外透射光谱的亚健康水心苹果检测

Detection of sub-healthy apples with watercore based on visible/near-infrared transmission spectroscopy

[目的]实现亚健康水心苹果的无损检测。[方法]分别采用对数函数法和研究提出的幂函数法消除果实直径对光谱衰减的影响,并分别转换为格拉姆角差场图、格拉姆角和场图、马尔可夫变迁场图、递归图以及对称极坐标图,通过添加卷积注意力模块的ResNet50网络模型挖掘与水心程度相关的图像深层特征,经t分布随机邻域嵌入方法降维并聚类分析后,将最适宜图像特征输入到改进粒子群算法优化的支持向量机、极限学习机、k近邻和随机森林分类器进行水心苹果三分类。[结果]幂函数法消除直径对透射光谱影响的效果优于对数函数法,格拉姆角差场图像特征可视化后,轮廓系数、CH分数和戴维森堡丁指数分别为0.93,0.88,0.24,均优于其余图像转换方法。ResNet50-IPSO-ELM取得了最高的分类准确率,为96.8%,测试集中对3种水心程度苹果的总体判别准确率可达96.3%,平均查准率、平均查全率和平均加权调和均值分别为87.2%,95.8%,92.3%。[结论]该模型不仅对多数类的无水心果、健康水心果保持较高分类准确率,也对少数类的亚健康水心果具有较高的判别能力。

[Objective]Achieving non-destructive testing of sub-healthy watercore apples.[Methods]First,the logarithmic function method and the power function method proposed by this study were used to correct the sample spectra.Subsequently,the corrected data were converted into different images of gramian angular difference field(GADF),gramian angular summation field(GASF),markov transition field(MTF),recurrence plot(RP),symmetric dot pattern(SDP).Then,the ResNet50 network model with the convolutional block attention module(CBAM)was used to mine the deep image features related to the degree of watercore,which were downscaled by the t-distributed stochastic neighbor embedding(t-SNE)method and analyzed by clustering to determine the most suitable image transformation method.Finally,the most suitable image features were inputted into the improved particle swarm algorithm(IPSO)optimized support vector machine(SVM),extreme learning machine(ELM),k-nearest neighbour(KNN)and random forest(RF)classifier for the three-class classification of watercore apple.[Results]The results showed that the power function method was better than the logarithmic function method in eliminating the effect of diameter on the transmission spectrum.The silhouette coefficient(SC),the calinski harabasz score(CHS),and the davies-bouldin index(DBI)were 0.93,0.88 and 0.24 after visualization of the image features in the GADF,better than the rest of the image transformation methods.ResNet50-IPSO-ELM achieved the highest classification accuracy of 96.8%.The overall discrimination accuracy of the three watercore classes apples in the test set reached 96.3%,and the stable precision(SP),stable recall(SR),and stable F 1-score(SF)were 87.2%,95.8%,and 92.3%,respectively.[Conclusion]The model maintains a high classification accuracy for the majority class of apples without watercore and healthy apples with watercore and a high discriminatory ability for the minority class of sub-healthy apples with watercore.