基于CARS-CNN的高光谱柑橘叶片含水率可视化研究

doi:10.3964/j.issn.1000-0593(2022)09-2848-07

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Abstract Water deficit of citrus leaves is one of the important factors affecting the growth of citrus. In order to study the effect of water stress on the moisture content of citrus, hyperspectral technology was used to rapidly and non-destructively detect the moisture content of citrus leaves, and pseudo-color processing was applied to realize the visualization of moisture content. 100 citrus leaves were collected, and 500 leaves with different gradient moisture content were obtained by drying method. The samples were divided into a training set (350 samples) and a testing set (150 samples) according to the ratio of 7∶3. The determination coefficient (R²) and root mean square error (RMSE) was used to evaluate the model’s prediction quality. A convolution neural network (CNN) is used to predict spectrum data. The CNN model uses a one-dimensional convolution kernel with three convolution pooling layers activated by the RELU activation function. The output layer uses a linear activation function for regression prediction, and the nadam algorithm is used to optimize and update the model with 1 000 epochs; The Raw spectrum data and the spectrum data are pretreated by SG, MSC and SNV are used respectively. The full bands, the feature bands screened by CARS and the feature bands extracted by PCA are imported into the CNN model respectively. The best model is CARS-CNN of the Raw spectrum data, the R²_c and RMSEC of the training set are 0.967 9 and 0.016 3 respectively. The R²_v and RMSEV of the testing set are 0.947 0 and 0.021 4, respectively. The effect of the full bands CNN model of the Raw spectrum data is the second, and the R²_c and RMSEC of the training set are 0.934 3 and 0.024 9, respectively. The R²_v and RMSEV of the testing set are 0.915 9 and 0.028 6, respectively; At the same time, the best combined results of the support vector machine regression model (SVR), partial least squares regression model (PLSR) and random forest model (RF) under different pretreatment methods and characteristic wavelength selection were compared. The best combination model (Raw spectrum+CARS+PLSR, SNV+PCA+RF, SNV+PCA+SVR) was compared with CARS-CNN of Raw spectrum data, CARS-CNN model still has the best prediction effect. Compared with other models, the CARS-CNN model has higher prediction accuracy than SVR, PLSR and RF models, after further feature extraction by CARS and convolution kernel. Select the trained CARS-CNN model, import the hyperspectral image into the model, calculate the moisture content of each pixel, and get the pseudo-color image, which can more intuitively display the visual distribution of leaf moisture content. The result provides a faster, more intuitive and more comprehensive assessment of citrus leaf moisture content, a basis for the study of citrus leaf water stress, and a reference for optimising intelligent irrigation decision-making.

Key words：Citrus leaf; Moisture content; Hyperspectral; Convolution neural network; Visualization

Received: 2021-07-28 Accepted: 2021-10-26

ZTFLH:

O657.39

Corresponding Authors: LI Zhen E-mail: lizhen@scau.edu.cn

Cite this article:

DAI Qiu-fang,LIAO Chen-long,LI Zhen, et al. Hyperspectral Visualization of Citrus Leaf Moisture Content Based on CARS-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2848-2854.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2022)09-2848-07 OR https://www.gpxygpfx.com/EN/Y2022/V42/I09/2848