Hyperspectral and Multispectral Co-inversion of Chlorophyll Content in Maize Leaves Based on Two-branch Convolutional Network

WANG Yazhou, XIAO Zhiyun


Aiming at the problem of accurate chlorophyll prediction in smart agriculture, a method of hyperspectral and multispectral synergistic inversion of chlorophyll content in maize leaves was proposed based on two-branch network. The undercomplete self-encoder was used for data dimensionality reduction to capture the most significant features in the data, so that the dimensionality reduced data can be trained instead of the original data to accelerate the training efficiency, and the two-branch convolutional network was used to fill the hyperspectral data with multispectral data to make full use of the spatial detail information of the hyperspectral data, and then combined with the 1DCNN to establish a prediction model of chlorophyll content in maize leaves. The results showed that compared with the traditional dimensionality reduction algorithm, the undercomplete self-encoder processed the best prediction results, with a coefficient of determination R2 of 0.988 and a root mean square error (RMSE) of 0.273, indicating that dimensionality reduction using the undercomplete self-encoder was effective in improving the accuracy of data inversion. Compared with the single hyperspectral data inversion model and the multispectral data inversion model, the two-branch convolutional network prediction models both achieved better prediction results, with R2 above 0.932 and RMSE below 1.765, indicating that the collaborative hyperspectral and multispectral image inversion model based on the two-branch convolutional network can make effective use of the features of the data. For the other data combined with the mentioned two-branch convolutional network model for the inverse model, the R2 was above 0.905 and the RMSE was below 2.149, which indicated that the prediction model had a certain degree of universality.

Keywords: maize leaves, chlorophyll content  hyperspectra, two-branch convolutional network, autoencoder, co-inversion


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