Spectroscopy and Spectral Analysis, Volume. 42, Issue 9, 2848(2022)
Hyperspectral Visualization of Citrus Leaf Moisture Content Based on CARS-CNN
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
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. Hyperspectral Visualization of Citrus Leaf Moisture Content Based on CARS-CNN[J]. Spectroscopy and Spectral Analysis, 2022, 42(9): 2848
Category: Research Articles
Received: Jul. 28, 2021
Accepted: Oct. 26, 2021
Published Online: Nov. 17, 2022
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