In recent years,the rapid development of modern spectral detection technology is closely related to deep learning.As an end-to-end model,the deep neural network can get more information from the spectra,thus improving the robustness of the model.A one-dimensional residual neural network(1D-AD-ResNet-18)model based on a convolutional block attention module was proposed to explore the feasibility of qualitative prediction of mango species by near-infrared spectroscopy combined with deep learning.Firstly,to reduce the interference of redundant information in the spectra,the CBAM convolution attention module is added to the traditional one-dimensional residual neural network,which can focus on the local useful information of the spectra.Secondly,to avoid the disappearance of gradient and the occurrence of overfitting,ResNet-18 is used to solve the problem of network"degradation".For 186 mango samples,70％of the samples were trained,and 30％were tested.Accuracy,Precision,Recall,Fl-score,Macro-average,and weighted average were used as evaluation indexes of the model.Three comparison models were established,including traditional one-dimensional ResNet-18,SNV-SVM,and PCA-KNN.Compared with the above three methods,the established 1D-AD-Res Net-18 model obtained the optimal prediction results,and the accuracy of the four qualitative analysis models was 96.42％,80.35％,76.78％and 67.85％.The experimental results show that the 1D-AD-ResNet-18 model can accurately identify and classify mango species,which provides a new idea for the qualitative analysis of mango species by NIR spectroscopy.
Mango species identificationCBAM attention mechanismNear-infrared spectroscopyResidual network
维普
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