Machine Learning Techniques Based on Ensemble Feature Selection for Disease Indentification and Classification in Plant Leaves
Keywords:
optimal Feature Selection, Graph Cut-based Multi-level Otsu, Kernel Fuzzy C Means, multiscale retinex algorithmAbstract
Farmers have a number of challenges when trying to examine vast regions for plant diseases manually. This is because it takes a lot of time and needs a big number of experienced labourers with a true grasp of plant diseases. In order to diagnose plant diseases accurately and quickly, image processing and machine learning models might be used. Agricultural specialists now use visual or microscopic examination of leaves or certain chemical methods to diagnose plant diseases. Large farms need a large crew of specialists and continual plant monitoring, both of which are prohibitively costly for the typical farmer. Managing plant diseases is essential for increasing crop yields and ensuring a healthy food supply. To begin with, GCMO, or Graph Cut-based Multi-level Otsu, is a variation of unsupervised multi-stage segmentation that this study suggests. It combines Graph Cut and Multi-Level Otsu algorithms. After that, several evaluation metrics are used to compare the segmentation performance of the proposed technique with current unsupervised segmentation algorithms. The pictures of rice, peanut, apple, and potato plant leaves are used for this purpose. When compared to preexisting unsupervised methods, the segmentation accuracies achieved by the suggested approach were much higher when evaluated on a variety of conventional and real-time datasets. This study's primary goals are to apply deep learning-based classification to pre-processed results and to adopt optimum Feature Selection (FS) to segmented ones. Kernel Fuzzy C Means (KFCM) is used for leaf image segmentation and the multi-level Otsu Thresholding approach is used for impacted area segmentation after the input pictures are pre-processed using a multiscale retinex algorithm.
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