Unsupervised Machine Learning for Clustering the Infected Leaves Based on the Leaf-Colors

Abstract

In data mining, the clustering is one of the important processes for categorizing the elements into groups whose associated members are similar in their features. In this paper, the plant leaves are grouped based on the colors in the leaves. Totally, three categories are specified to represent the leaf with more green, leaf with yellowish shades and leaf with reddish shades. The task is performed using image processing. The leaf images are processed in the sequence such as image preprocessing, segmentation, feature extraction, and clustering. Preprocessing is done to denoize, enhance, and background color fixing for betterment of result. Then, the color-based segmentation is done on the preprocessed image for generating the sub-images by clustering the pixels based on the colors. Next, the basic features such as entropy, mean, and standard deviation are extracted from each sub-images. The extracted features are used for clustering the images based on the colors. The image clustering is done by the Neural Network architecture, self-organizing map (SOM), and K-Means algorithm. They are evaluated with various distance measuring functions. Finally, the city-block in both method produced the clusters with same size. This cluster set can be used as a training set for the leaf classification in future.