Tree Species Recognition Based on Overall Tree Image and Ensemble of Transfer Learning

被引：0
|
作者：
Feng H. [1 ,2 ]
Hu M. [1 ,3 ]
Yang Y. [1 ,2 ]
Xia K. [1 ,3 ]
机构：
[1] School of Information Engineering, Zhejiang A&F University, Hangzhou
[2] Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou
[3] Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2019年 / 50卷 / 08期
关键词：
Deep learning; Ensemble learning; Image recognition; Transfer learning; Tree species recognition;
D O I：
10.6041/j.issn.1000-1298.2019.08.025
中图分类号：
学科分类号：
摘要：
The automatic classification and recognition of tree image has important practical application value. Relevant research on traditional tree species recognition includes leaf recognition, flower recognition, bark texture recognition, and wood texture recognition. In order to solve the problem of recognizing the tree image with complex background in nature scenes, a tree species recognition method based on the overall tree image and ensemble of transfer learning was proposed. Four pre-training models of AlexNet, VggNet-16, Inception-V3 and ResNet-50 were firstly used on ImageNet large-scale datasets to extract features. They were then transferred to the target tree dataset to train four different classifiers. An ensemble model was finally established by the relative majority voting method and the weighted average method. A new tree image dataset called TreesNet was built and experiments were designed based on the dataset, including the comparative experiments of transfer learning and conventional methods.The experimental results showed that data augmentation can effectively solve the over-fitting problem and the training model had better generalization ability and higher recognition rate. The image recognition accuracy of the tree species in the complex background with the method proposed reached 99.15%, which had a better effect on overall tree image recognition compared with the conventional classification methods of K-nearest neighbor (KNN), support vector machine (SVM) and back propagation neural network (BP). © 2019, Chinese Society of Agricultural Machinery. All right reserved.
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页码：235 / 242and279
相关论文
共 22 条
  • [1] Zheng Y., Zhong G., Wang Q., Et al., Method of leaf identification based on multi-feature dimension reduction, Transactions of the Chinese Society for Agricultural Machinery, 48, 3, pp. 30-37, (2017)
  • [2] Hu M.K., Visual pattern recognition by moment invariants, Information Theory IRE Transactions On, 8, 2, pp. 179-187, (1962)
  • [3] Wang L., Huai Y., Peng Y., Method of identification of foliage from plants based on extraction of multiple features of leaf images, Journal of Beijing Forestry University, 37, 1, pp. 55-61, (2015)
  • [4] Zhang S., Huai Y., Leaf image recognition based on layered convolutions neural network deep learning, Journal of Beijing Forestry University, 38, 9, pp. 108-115, (2016)
  • [5] Barthelemy D., Boujemaa N., Mathieu D., Et al., The PlantNet project: plant computational identification and collaborative information system, XVIII 18th International Botanical Congress, (2011)
  • [6] Guan Y., Flower species recognition system based on residual network transfer learning, Computer Engineering and Applications, pp. 1-9
  • [7] Deng J., Dong W., Socher R., Et al., Imagenet: a large-scale hierarchical image database, Computer Vision and Pattern Recognition, pp. 248-255, (2009)
  • [8] Zheng Y., Zhang L., Plant leaf image recognition method based on transfer learning with convolutional neural networks, Transactions of the Chinese Society for Agricultural Machinery, 49, pp. 354-359, (2018)
  • [9] Krizhevsky A., Sutskever I., Hinton G.E., ImageNet classification with deep convolutional neural networks, Communications of the ACM, 60, 2, (2013)
  • [10] Szegedy C., Vanhoucke V., Ioffe S., Et al., Rethinking the inception architecture for computer vision, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818-2826, (2016)
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