Breaking Out of the Black Box in Automated Flower Recognition

Currently, fully automated methods for plant recognition are being developed. These systems work based on images. But, they only give the name of the plants as a result and do not give an explanation (black box approach). On the contrary, giving the explanation (by describing the plant's characteristics) formally is used by taxonomists before they can determine the plant's name. Providing the explanation is very important since it fit in the standard practice and as a basis of plant identification for several decades. This paper comes with the new perspective in automated image-based plant recognition. A system that acts not as a black box system has proposed. It can mimic the taxonomist in explaining the decision and giving useful alternatives. The research firstly focuses on the flower. In the first part of this paper, the background of the new perspective is presented. Then, some problems that we want to answer also will be shown. Finally, some solutions to achieve the goal are discussed. As the first attempts, we also conduct a little experiment using the decision tree to deal with the goal.