Leaf Disease Detection using Polynomial SVM and Euclidean Distance Metric

Abstract

Abstract: Agriculture productivity is highly important in the world to survive. There are lots of involvements of artificial intelligence in agriculture to help the productivity. Automatic leaf disease detection is one of them. It is hard to diagnose the leaf disease by normal vision because it looks quite natural. If care is not taken properly then it directly affects the quality of the production. So, it is important to detect the disease at early stage through which production can be improved and proper care can be taken place. There are so many researches that have been done in this field but there are certain flaws present in the resulting system. Proposed system is based on Polynomial SVM (Support Vector Machine) and an Euclidean Distance Metric. Polynomial SVM is a classifier that can handle the non-linear data in a very effective manner. Euclidean Distance Metric calculates distance between two different clusters or points; through which decision can be made easily. Dataset has been taken from kaggle for four different categories; such as Alternaria Alternata, Bacterial Blight, Cercospora Leaf Spot and Healthy Leaves. The proposed method provides 97.30% of accuracy which is bit higher than the KNN classifier