Analysis of Crop disease detection with SVM, KNN and Random forest classification

Due to an uneven climatic condition crops are being affected which leads decrease in agriculture yield. It greatly affects global agricultural economy. However, the becomes more worse when diseases identified crops. Agriculture plays a vital role every country’s Thus, there is need identify crop disease before it visible on so that can be avoided by using appropriate measures. The traditional way of identifying through observation naked eyes. But as requires large number experts and continuous monitoring will costly for fields. Hence, automatic system required not only examine detect but also classify type proposed determines from input image. image has go following stages: Image Acquisition, pre-processing, segmentation, Feature Extraction, Classification order determine diseased accordingly provides remedy disease. Infected taken Acquisition stage. In pre-processing stage noise removed applying gaussian blur filter non-local means denoising algorithm. Also, background eliminated Thresholding To extract Region Interest (ROI) i.e. infected region K-means Clustering algorithm used. Then color, texture shape features extracted ROI further send classification Three different algorithms namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN) Random Forest implemented out Algorithm found best terms accuracy. carried Multivariate present accurately. this way, detects given