Can bipartite graph be effective tool for clustering: A self-organizing map-based automated plant disease detection

An innovative approach for automated plant disease identification has been proposed in this study. The main contribution of this study is the introduction of a bipartite graph-based clustering technique that has been used for image segmentation, a feature extraction methodology using Self-Organizing Map (SOM), and a ray tracing method. Numerous research works have already been done in this area with their respective merits and demerits. But this bipartite graph-based clustering for image segmentation and feature extraction using SOM and ray tracing techniques has not been used in any of these studies as far as we are aware. The core idea behind this clustering technique is to represent similar spatial data points using a bipartite graph and then singular value decomposition has been used on that graph for clustering. It is common to use SOM for clustering. However, in this study, SOM has been used for feature extraction. First, a spatial dependency matrix based on the pixel value of the gray image has been constructed using SOM. Then some statistical features have been computed from this matrix. Using the ray tracing method, the length of the most extended cluster, i.e. the length of the most extended disease-affected patch, and the distribution of the clusters in the image have been computed. The accuracy of our model has been greatly enhanced using these features only. It has been experimented on disease-affected grape leaf images taken from the Plant Village Dataset. This model outperforms state-of-the-art models, which is shown in the experimental findingsresults section. Not only that, the proposed features produce better accuracy rather than using some existing features. This comparison has also been shown in the experimental findings. The result has been validated using K-fold cross-validation. Last, but not the least, these features produce good accuracy using different classifiers also.