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Establishment of deep learning model for the growth of tea cutting seedlings based on hyperspectral imaging technique

文献类型： 外文期刊

作者： Li, He 1 ; Mao, Yilin 2 ; Shi, Hongtao 3 ; Fan, Kai 2 ; Sun, Litao 1 ; Zaman, Shah 1 ; Shen, Jiazhi 1 ; Li, Xiaojiang 1 ; Bi, Caihong 4 ; Shen, Yaozong 2 ; Xu, Yang 2 ; Chen, Hao 2 ; Ding, Zhaotang 1 ; Wang, Yu 2 ;

作者机构： 1.Shandong Acad Agr Sci, Tea Res Inst, Jinan 276800, Peoples R China

2.Qingdao Agr Univ, Tea Res Inst, Qingdao 266109, Peoples R China

3.Qingdao Agr Univ, Sch Sci & Informat Sci, Qingdao 266109, Peoples R China

4.Linyi Agr & Rural Bur, Agr Technol Extens Ctr, Linyi 276037, Peoples R China

关键词： Tea cutting seedings; Hyperspectral imaging technique; Biomass; Nondestructive testing; Deep learning

期刊名称：SCIENTIA HORTICULTURAE （ 影响因子：4.3； 五年影响因子：4.5 ）

ISSN： 0304-4238

年卷期： 2024 年 331 卷

页码：

收录情况： SCI

摘要： The shoot and root growth not only serve as important indicators for measuring the quality of tea cutting seedlings, but also can provide scientific basis for agricultural production and fine seedling management. Currently, manual weighing is the main method used to measure shoot and root growth, which is both destructive and inefficient. The study proposes a method for monitoring the shoot and root growth in tea cutting seedlings using hyperspectral imaging. First, mature leaves and shoots spectra were extracted from tea cutting seedlings using Mask R-CNN (Epoch=20 and Learning-rate=0.001). The spectra were then preprocessed by MSC, S-G, 1-D filtering techniques, with feature bands screened by UVE (Optimal factor number=5), CARS (Monte Carlo sampling times=300), and SPA (Epoch=25). Finally, a CNN-GRU (Epoch=100 and Learning-rate=0.01) network was employed to predict shoot and root biomass, and compared with machine learning methods of SVR (Kernel function=Polynomial), RFR (Ntrees=200), and PLSR (Latent variable=16) and deep learning methods (Epoch=100 and Learning-rate=0.01) of CNN and LSTM. The results show that (i) Mask R-CNN can accurately extract the spectral of mature leaves (precision=97.8 %) and shoots (precision=91.5 %), (ii) The spectral feature bands of shoot (number=212) and root (number=105) biomass screened by UVE were more abundant than CARS and SPA, (iii) The UVE+CNN-GRU model (Rp2=0.90, RMSEP = 0.12, RPD=2.43) based on shoots spectrum provided the optimal estimation of shoot biomass. The SPA+LSTM model (Rp2 = 0.65, RMSEP = 0.05, RPD=1.67) based on the mature leaves spectrum provided the optimal estimation of root biomass. These findings demonstrate that hyperspectral imaging technology combined with deep learning algorithms can accurately monitor the growth of cutting seedlings quickly without causing damage. This not only provides a new data sources along with technical means for efficient screening excellent tea varieties, but also improves agricultural production efficiency and resource utilization.

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