Author(s)

Takeshi Ise^1,2*, Mari Minagawa³, Masanori Onishi⁴

¹Field Science Education and Research Center, Kyoto University, Kyoto, Japan.
²PRESTO, Japan Science and Technology Agency, Kawaguchi, Japan.
³Faculty of Agriculture, Kyoto University, Kyoto, Japan.
⁴Graduate School of Agriculture, Kyoto University, Kyoto, Japan.

Especially in recent years, deep learning has become a very effective tool for object identification. However, in general, the automatic object identification tends not to work well on ambiguous, amorphous objects such as vegetation. In this study, we developed a simple but effective approach to identify ambiguous objects and applied the method to several moss species. The technique called chopped picture method, where teacher images are systematically dissected into numerous small squares. As a result, the model correctly classified 3 moss species and “non-moss” objects in test images with accuracy more than 90%. Using this approach will help progress in computer vision studies for various ambiguous objects.

Remote Sensing, Classification, Deep Learning, Object Identification

Ise, T. , Minagawa, M. and Onishi, M. (2018) Classifying 3 Moss Species by Deep Learning, Using the “Chopped Picture” Method. Open Journal of Ecology, 8, 166-173. doi: 10.4236/oje.2018.83011.