PLANT IDENTIFICATION BASED ON A LATE FUSION METHOD  WITH PRIORITY WEIGHTS

Nguyễn Thị Thanh Nhàn; Nguyễn Thu Hương

PLANT IDENTIFICATION BASED ON A LATE FUSION METHOD WITH PRIORITY WEIGHTS

About this article

Received: 29/01/21 Revised: 28/02/21 Published: 28/02/21

Authors

1. Nguyen Thi Thanh Nhan , TNU - University of Information and Communication Technology
2. Nguyen Thu Huong, TNU - University of Information and Communication Technology

Abstract

Automatic plant identification based on images is currently very interesting. The challenge with the plant identification problem is the great similarity among species, especially when the number of species is large. In this study, we developed a plant identification method based on the use of late fusion method for identification results on different plant organs. We assigned priority weights by organ/species to the confidence scores of each model. The organ/species with a better identification result was assigned a higher weight. GoogLeNet was used to identify plant based on each organ. Experiments were applied to combine two to six organs according to leaf, flower, fruit, stem, branch, entire. This method is based on combining the product rule using weights assigned to plant organs and species. The experimental results have shown the effectiveness of the proposed method, it outperforms some fusion late methods. The proposed method achieved the highest accuracy when combining 2 organs, 3 organs, 4 organs, 5 organs, and 6 organs with 96.0%, 98.2%, 98.8%, 99%, and 99.2% respectively.

Keywords

Late fusion; Product rule; Plant identification; Convolutional neural network; Priority weights

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References

[1] W. Jana and P. Mäder, "Plant species identification using computer vision techniques: A systematic literature review," Archives of Computational Methods in Engineering, vol. 25, no. 2, pp. 507-543, 2018.

[2] H. Goëau, P. Bonnet, and A. Joly, "Lifeclef plant identification task 2015," in CEUR-WS (Ed.), CLEF: Conference and Labs of the Evaluation forum, vol. 1391 of CLEF2015 Working notes, Toulouse, France, 2015.

[3] H. Goëau, P. Bonnet, and A. Joly, “Plant identification in an open-world (lifeclef 2016),” CLEF working notes 2016, pp. 428-439.

[4] H. Goëau, P. Bonnet, and A. Joly, “Plant identification based on noisy web data: the amazing performance of deep learning (lifeclef 2017),” CEUR Workshop Proceedings, 2017.

[5] J. Kittler, M. Hatef, R. P. Duin, and J. Matas, “On combining classifiers,” IEEE transactions on pattern analysis and machine intelligence, vol. 20, no. 3, pp. 226-239, 1998.

[6] T. T. N. Nguyen, T. L. Le, and H. Vu, “Do we need multiple organs for plant identification?” 2020 International Conference on Multimedia Analysis and Pattern Recognition (MAPR), IEEE, 2020, pp. 1-6.

[7] A. He, and X. Tian, “Multi-organ plant identification with multi-column deep convolutional neural networks,” 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016, pp. 002020-002025.

[8] H. T. Vo, G.-H. Yu, T. V. Dang, and J. -Y. Kim, "Late fusion of multimodal deep neural networks for weeds classification," Computers and Electronics in Agriculture, vol. 175, 2020, Art. no. 105506.

[9] A. Jain, K. Nandakumar, and A. Ross, "Score normalization in multimodal biometric systems," Pattern recognition, vol. 38, no. 12, pp. 2270-2285, 2005.

[10] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1-9.

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