CNN-based tree species identification from bark image

Junya Ido; Takeshi Saitoh

doi:10.1117/12.2524213

6 May 2019 CNN-based tree species identification from bark image

Junya Ido, Takeshi Saitoh

Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 110693G (2019) https://doi.org/10.1117/12.2524213
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China

Abstract

This paper proposes a convolutional neural network (CNN)-based tree species identification method from bark image. The proposed method uses the well-known CNN model. The difficulty of our problem is to use a special tree image in which a colorful tag is stick on the bark. The tag is irrelevant to the species. In order to recognize with CNN, it is necessary to extract a region (ROI) excluding the tag. Thus, this paper proposes a ROI extraction method. Extracted ROI is fed to CNN. We evaluated the proposed method with six tree species. We carried out the evaluation experiment with various conditions, and found an optimal condition for our problem.

Citation Download Citation

Junya Ido and Takeshi Saitoh "CNN-based tree species identification from bark image", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110693G (6 May 2019); https://doi.org/10.1117/12.2524213

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