Open AccessA multiple strategy for plant species identification using images of leaf texture
Author(s) -
Nubia Rosa,
Igor Luidji,
Sérgio Da Silva,
Douglas Farias
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.52591/202107243
Subject(s) - robustness (evolution) , artificial intelligence , feature extraction , computer science , segmentation , plant identification , pattern recognition (psychology) , plant species , image texture , texture (cosmology) , fractal , computer vision , quantization (signal processing) , image segmentation , mathematics , image (mathematics) , botany , biology , mathematical analysis , biochemistry , gene
In our planet there are thousands of plant species, being important to catalog these to help in the biodiversity preservation. However, identifying various plant species is not an easy task, even for specialists. Methods of computer vision for identifying plant species are interesting solutions for these difficulties. This work aims to analyze the efficiency of texture feature extraction methods applied in the identification of plant species by means of images of its leaves. For this, different texture descriptors were applied in three different databases. The obtained results indicate that local phase quantization (LPQ)-based methods achieve great efficiency and robustness. Additionally, the combination of LPQ-based methods with a segmentation based fractal texture analysis (SFTA) has increased the correct classification rate in all databases.