Open AccessCoffee plant image segmentation and disease detection using JSEG algorithm
Author(s) -
Jeferson de Souza Dias,
José Hiroki Saito
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/wvc.2021.18887
Subject(s) - image segmentation , segmentation , histogram , scale (ratio) , artificial intelligence , similarity (geometry) , pattern recognition (psychology) , computer science , tree (set theory) , homogeneous , rust (programming language) , image (mathematics) , mathematics , computer vision , geography , cartography , mathematical analysis , combinatorics , programming language
Brazil is the largest coffee producer in the world, and then there are many challenges to maintain the high quality and purity of the beans. Thus, it is important to study coffee plants, and help agronomists to detect diseases, such as rust, with resources of computer science. In this work, it is described experiments using image segmentation algorithm JSEG, which is capable to segment images in multi-scale. Using a coffee tree image database RoCoLe (Robusta Coffee Leaf Images), the JSEG algorithm is used to segment these images in four scales. It is selected typical segments in each scale and they are grouped using similarity of normalized color histograms. In this way the several scales segmentations are compared. It is concluded that the segments in scales 1 and 2, in which the colors are more homogeneous then in scales 3 and 4, are adequate to use as training samples for the detection of rust diseases.