Tree species identification based on the fusion of bark and leaves

Yafeng Zhao; Xuan Gao; Junfeng Hu; Zhen Chen; Yafeng Zhao; Xuan Gao; Junfeng Hu; Zhen Chen

doi:10.3934/mbe.2020222

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 4: 4018-4033. doi: 10.3934/mbe.2020222

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Research article

Tree species identification based on the fusion of bark and leaves

1.
College of Information and Computer Engineering, Northeast Forestry University, Harbin 154000, China
2.
College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 154000, China

Received: 25 March 2020 Accepted: 26 May 2020 Published: 02 June 2020

For trees, leaves are often used for identification, but the shape of leaves changes greatly, bark will be another identifying feature. However, it is difficult to recognize by a single organ when there are intra class differences and inter class similarities between leaves or bark. So we fuse features of leaf and bark. Firstly, we collected 17 species of leaves and bark of trees through field shooting and web crawling. Then propose a method of combining convolution neural network (CNN) with cascade fusion, additive fusion algorithm, bilinear fusion and score level fusion. Finally, the features extracted from the leaves and bark are fused in the ReLu layer and Fully connected layer. The method was compared with single organ recognition, Support Vector Machines (SVM), and existing fusion methods, results show that the two organ fusion method proposed are better than the other recognition methods, and recognition accuracy is 87.86%. For similar trees, when it is impossible to accurately determine its species by a single organ, the fusion of two organs can effectively improve this situation.
- tree species,
- leaf,
- bark,
- SVM,
- CNN,
- organ fusion
Citation: Yafeng Zhao, Xuan Gao, Junfeng Hu, Zhen Chen. Tree species identification based on the fusion of bark and leaves[J]. Mathematical Biosciences and Engineering, 2020, 17(4): 4018-4033. doi: 10.3934/mbe.2020222

Related Papers:

Abstract

For trees, leaves are often used for identification, but the shape of leaves changes greatly, bark will be another identifying feature. However, it is difficult to recognize by a single organ when there are intra class differences and inter class similarities between leaves or bark. So we fuse features of leaf and bark. Firstly, we collected 17 species of leaves and bark of trees through field shooting and web crawling. Then propose a method of combining convolution neural network (CNN) with cascade fusion, additive fusion algorithm, bilinear fusion and score level fusion. Finally, the features extracted from the leaves and bark are fused in the ReLu layer and Fully connected layer. The method was compared with single organ recognition, Support Vector Machines (SVM), and existing fusion methods, results show that the two organ fusion method proposed are better than the other recognition methods, and recognition accuracy is 87.86%. For similar trees, when it is impossible to accurately determine its species by a single organ, the fusion of two organs can effectively improve this situation.

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