Identifying 3 moss species by deep learning, using the "chopped picture" method
This work addresses a specific challenge in computer vision for botanists or ecologists, but it is incremental as it applies an existing method to a new domain.
The study tackled the problem of identifying ambiguous, amorphous objects like vegetation by developing a simple but effective approach, achieving over 90% accuracy in classifying test images for three moss species.
In general, 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. As a result, the model correctly classified test images with accuracy more than 90%. Using this approach will help progress in computer vision studies.