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Tea Category Identification Using Wavelet Signal Reconstruction of Hyperspectral Imagery and Machine Learning

Author

Listed:
  • Qiang Cui

    (School of Information and Computer, Anhui Agricultural University, Hefei 230036, China)

  • Baohua Yang

    (School of Information and Computer, Anhui Agricultural University, Hefei 230036, China)

  • Biyun Liu

    (School of Information and Computer, Anhui Agricultural University, Hefei 230036, China)

  • Yunlong Li

    (School of Information and Computer, Anhui Agricultural University, Hefei 230036, China)

  • Jingming Ning

    (State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, 130 Changjiang West Road, Hefei 230036, China)

Abstract

Accurately distinguishing the types of tea is of great significance to the pricing, production, and processing of tea. The similarity of the internal spectral characteristics and appearance characteristics of different types of tea greatly limits further research on tea identification. However, wavelet transform can simultaneously extract time domain and frequency domain features, which is a powerful tool in the field of image signal processing. To address this gap, a method for tea recognition based on a lightweight convolutional neural network and support vector machine (L-CNN-SVM) was proposed, aiming to realize tea recognition using wavelet feature figures generated by wavelet time-frequency signal decomposition and reconstruction. Firstly, the redundant discrete wavelet transform was used to decompose the wavelet components of the hyperspectral images of the three teas (black tea, green tea, and yellow tea), which were used to construct the datasets. Secondly, improve the lightweight CNN model to generate a tea recognition model. Finally, compare and evaluate the recognition results of different models. The results demonstrated that the results of tea recognition based on the L-CNN-SVM method outperformed MobileNet v2+RF, MobileNet v2+KNN, MobileNet v2+AdaBoost, AlexNet, and MobileNet v2. For the recognition results of the three teas using reconstruction of wavelet components LL + HL + LH, the overall accuracy rate reached 98.7%, which was 4.7%, 3.4%, 1.4%, and 2.0% higher than that of LH + HL + HH, LL + HH + HH, LL + LL + HH, and LL + LL + LL. This research can provide new inspiration and technical support for grade and quality assessment of cross-category tea.

Suggested Citation

  • Qiang Cui & Baohua Yang & Biyun Liu & Yunlong Li & Jingming Ning, 2022. "Tea Category Identification Using Wavelet Signal Reconstruction of Hyperspectral Imagery and Machine Learning," Agriculture, MDPI, vol. 12(8), pages 1-16, July.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:8:p:1085-:d:869839
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    Citations

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    Cited by:

    1. Lichao Liu & Quanpeng Bi & Jing Liang & Zhaodong Li & Weiwei Wang & Quan Zheng, 2022. "Farmland Soil Block Identification and Distribution Statistics Based on Deep Learning," Agriculture, MDPI, vol. 12(12), pages 1-17, November.
    2. Gniewko Niedbała & Sebastian Kujawa, 2023. "Digital Innovations in Agriculture," Agriculture, MDPI, vol. 13(9), pages 1-10, August.
    3. Jianghua Ye & Qi Zhang & Miao Jia & Yuhua Wang & Ying Zhang & Xiaoli Jia & Xinyu Zheng & Haibin Wang, 2024. "The Effects of Rock Zones and Tea Tree Varieties on the Growth and Quality of Wuyi Rock Tea Based on the OPLS-DA Model and Machine Learning," Agriculture, MDPI, vol. 14(4), pages 1-14, April.
    4. Na Luo & Yunlong Li & Baohua Yang & Biyun Liu & Qianying Dai, 2022. "Prediction Model for Tea Polyphenol Content with Deep Features Extracted Using 1D and 2D Convolutional Neural Network," Agriculture, MDPI, vol. 12(9), pages 1-16, August.

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