Adoptive Clustering Algorithm with Feature Subset Selection Method to find the Plant Diseases
DOI:
https://doi.org/10.26438/ijcse/v7i11.198202Keywords:
Machine learning (ML), ACA, AI and K-MeansAbstract
Machine Learning (ML) is the subfield in Artificial Intelligence (AI) that works dynamically to solve several issues. ML mainly focused on understanding the structure of the data and selecting the specific model based on the given dataset. Nowadays plant diseases are becoming very dangerous to farmers. Various plant diseases are identified by many researchers based on the pathogen. Several visible and invisible features are present to identify plant diseases. Visible features such as shape, size, silting are most widely used to analyze the condition of the plant. In this paper, the adaptive clustering algorithm (ACA) is introduced to detect diseases in plants. To show the disease-affected region the fuzzy c-means (FCM) clustering approach is adopted to highlight the disease-affected region with red patches which are called clusters. To improve the performance of the proposed approach the feature subset selection is used to increase the effectiveness and scalability. The output results show the performance of the ACA.
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