Feature Selection Using Louvain Clustering Algorithm for Rice Disease Prediction

Rice is the most common staple food consumed worldwide by the humans. But the growth of this grain gets severely affected by various diseases. Several microorganisms like virus, bacteria and fungi cause the rice diseases mainly. The increasing demand on rice is going parallel with the rapid development of rice disease prediction during the past few years. Therefore, a study is needed for facilitating the procedure of identifying and preventing the diseases. This paper gives a clear idea about several rice diseases and how these diseases can be reduced so that agriculture gets less affected. Here, several features are extracted from the images of diseased rice plants and a weighted graph is generated where nodes of the graph are the extracted features and weights are the similarity between each pair of features. Then, Louvain community detection algorithm is used to partition the graph. The highest-ranked node from each partition of the graph is selected as the most important node in the partition. Thus, a subset of features is determined with the help of the graph. The proposed method shows its importance with successful outcome for rice disease prediction.

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