Affiliations: Department of Informatics Systems and Networks, Faculty of Informatics, University of Debrecen, Kassai street, 26, 4028, Debrecen, Hungary | Tel.: +36 52 512 900/75016; E-mail: suto.jozsef@inf.unideb.hu
Correspondence:
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Corresponding author: Department of Informatics Systems and Networks, Faculty of Informatics, University of Debrecen, Kassai street, 26, 4028, Debrecen, Hungary. Tel.: +36 52 512 900/75016; E-mail: suto.jozsef@inf.unideb.hu.
Abstract: Nowadays there are hundreds of thousands known plant species on the Earth and many are still unknown yet. The process of plant classification can be performed using different ways but the most popular approach is based on plant leaf characteristics. Most types of plants have unique leaf characteristics such as shape, color, and texture. Since machine learning and vision considerably developed in the past decade, automatic plant species (or leaf) recognition has become possible. Recently, the automated leaf classification is a standalone research area inside machine learning and several shallow and deep methods were proposed to recognize leaf types. From 2007 to present days several research papers have been published in this topic. In older studies the classifier was a shallow method while in current works many researchers applied deep networks for classification. During the overview of plant leaf classification literature, we found an interesting deficiency (lack of hyper-parameter search) and a key difference between studies (different test sets). This work gives an overall review about the efficiency of shallow and deep methods under different test conditions. It can be a basis to further research.
