Coffee plant disease detection using JSEG segmentation and near sets clustering

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 clustered in similarity of normalized color histograms, using Near Sets theory. In this way the several scales segmentations are compared. It is concluded that smaller segments, scales 1 and 2, in which the colors are more homogeneous than in greater segments, scales 3 and 4, are adequate to use as training samples for the detection of rust diseases using artificial neural networks. The proposed approach can be applied for counting the segments affected by rust, to estimate the disease severity in coffee leaves.