Research on remote sensing recognition of wild planted Lonicera japonica based on deep convolutional neural network.
10.19540/j.cnki.cjcmm.20200927.103
- Author:
Ting-Ting SHI
1
;
Xiao-Bo ZHANG
1
;
Lan-Ping GUO
1
;
Zhi-Xian JING
1
;
Lu-Qi HUANG
1
Author Information
1. State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica,China Academy of Chinese Medical Sciences Beijing 100700, China.
- Publication Type:Journal Article
- Keywords:
GoogLeNet;
Lonicera japonica;
convolutional neural networks;
deep learning;
drone remote sensing
- MeSH:
Lonicera;
Neural Networks, Computer;
Remote Sensing Technology
- From:
China Journal of Chinese Materia Medica
2020;45(23):5658-5662
- CountryChina
- Language:Chinese
-
Abstract:
Identification of Chinese medicinal materials is a fundamental part and an important premise of the modern Chinese medicinal materials industry. As for the traditional Chinese medicinal materials that imitate wild cultivation, due to their scattered, irregular, and fine-grained planting characteristics, the fine classification using traditional classification methods is not accurate. Therefore, a deep convolution neural network model is used for imitating wild planting. Identification of Chinese herbal medicines. This study takes Lonicera japonica remote sensing recognition as an example, and proposes a method for fine classification of L. japonica based on a deep convolutional neural network model. The GoogLeNet network model is used to learn a large number of training samples to extract L. japonica characteristics from drone remote sensing images. Parameters, further optimize the network structure, and obtain a L. japonica recognition model. The research results show that the deep convolutional neural network based on GoogLeNet can effectively extract the L. japonica information that is relatively fragmented in the image, and realize the fine classification of L. japonica. After training and optimization, the overall classification accuracy of L. japonica can reach 97.5%, and total area accuracy is 94.6%, which can provide a reference for the application of deep convolutional neural network method in remote sensing classification of Chinese medicinal materials.
