Enhancement of template-based method for overlapping rubber tree leaf identification

Abstract

The position of rubber tree leaflets is one of the critical features for rubber clone classification. These leaflets exist in three possible positions: overlapping, touching, or separated. This paper proposed a template-based method for overlapping rubber tree leaf identification. Initially, the key point based feature extraction method is adopted. The key features of overlapping and non-overlapping leaf assist in identifying similar shapes through comparison, using the nearest neighbor algorithm. This process is implemented by constructing a directory which consists of various rubber leaf images with different positions. Next, the key points in the input leaf image are compared with the key points of the template image to identify the position of leaflets. The outcome of this study proves that the template-based method is suitable for overlapping and non-overlapping rubber tree leaf identification.