Fine hyperspectral classification of rice varieties based on attention module 3D-2DCNN

Rice is an indispensable food crop for human beings. Rice varieties are closely related to disease resistance, insect resistance, lodging resistance, grain quality and yield. Different varieties of rice have similar appearance traits and change trends, which are difficult to distinguish. It is of great significance to classify and identify rice varieties with high precision in a wide range by objective and non-destructive detection methods. In this paper, the canopy hyperspectral images of rice varieties were obtained by using the S185 hyperspectral imaging device mounted on a UAV platform. And the spectral and spatial features of 14 rice varieties were automatically learned and deeply extracted by hybrid convolutional neural network structure. In addition, in order to improve the performance of the model, the article attempts to optimize the model with the end-to-end trainable attention module. Finally, extensive experiments are carried out to prove the validity of the model. Compared with the advanced methods, the 3D-CSAM-2DCNN proposed in this paper performed the best classification on fine classification of rice varieties. The overall accuracy of 98.93% and the accuracy of more than 98.22% for single variety has been achieved. The proposed model is conducive to automatic identification of fields and crop phenotypes research, and contributes new possibilities to promote the development of precision agriculture and smart agriculture.

Automated summary

This paper obtains canopy hyperspectral images of rice varieties using a UAV platform with a S185 hyperspectral imaging device. A hybrid convolutional neural network structure is used to automatically analyze and extract spectral and spatial features of 14 rice varieties. Additionally, an attention module is applied to optimize the model. Extensive experiments demonstrate the validity of the proposed model, achieving high accuracy in fine classification of rice varieties. The model contributes to automatic field identification and crop phenotype research, and presents new possibilities for the development of precision agriculture and smart agriculture.