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Journal of Tea Science ›› 2023, Vol. 43 ›› Issue (3): 411-423.doi: 10.13305/j.cnki.jts.2023.03.006

• Research Paper • Previous Articles     Next Articles

Data Enhancement Optimization and Class Activation Mapping Quantitative Evaluation for CNN Image Recognition of Multiple Tea Categories

ZHANG Zhanyi1, ZHANG Baoquan1, WANG Zhouli1, YANG Yao1, FAN Dongmei1, HE Weizhong2, MA Junhui3,*, LIN Jie1,*   

  1. 1. College of Tea Science and Tea Culture, Zhejiang A&F University, Lin'an 311300, China;
    2. Lishui Academy of Agricultural and Forestry Sciences, Lishui 323000, China;
    3. Lishui Economic Crop Terminal, Lishui 323000, China
  • Received:2023-02-14 Revised:2023-04-19 Online:2023-06-15 Published:2023-06-29

Abstract: There are many kinds of tea in China, and subjective identification is easy to be confused and very dependent on professional experience. Convolutional Neural Network (CNN) image recognition applied to multi-tea identification has the advantages of objectivity, adaptability to complex image backgrounds and portability to mobile devices. However, the current CNN image recognition of tea lacks data enhancement optimization and objective evaluation of recognition accuracy, which limits the robustness and generalization ability of model recognition. In this study, a total of 6 123 images of 29 common tea categories were collected to construct a dataset, and the ResNet-18 (Residual network-18) training effects of 10 image data enhancement methods were compared. To objectively evaluate the accuracy of the model recognition area, two gradient-weighted class activation mapping (Grad-CAM ) quantitative evaluation indexes (IOB and MPI) were constructed. The results show that grid erasure (Ratio=0.3), resolution perturbation and HSV (Hue, Saturation, Value) color space perturbation are better data enhancement methods, with four indicators of accuracy, loss, IOB and MPI performing better. Furthermore, through the ablation experiment, the optimal combination of data enhancement methods “horizontal mirror flip + grid erasure (Ratio=0.3) + HSV color perturbation” was obtained. The accuracy rate of model test reached 99.82%, with a loss value of only 0.64, and the IOB and MPI indicators also performed better, reflecting good accuracy in image recognition. This study optimized the tea image data enhancement method, and obtained the multi-tea CNN image recognition model with high robustness. The constructed quantization indexes IOB and MPI also solved the problem of accuracy evaluation of CAM recognition region.

Key words: tea recognition, convolutional neural network, image recognition, data augmentation, class activation mapping

CLC Number: