Plant disease prediction using classification algorithms

<p>This paper investigates the capability of six existing classification algorithms (Artificial Neural Network, Naïve Bayes, k-Nearest Neighbor, Support Vector Machine, Decision Tree and Random Forest) in classifying predicting diseases soybean mushroom datasets using with numerical or categorical attributes. While many similar studies have been conducted on images to predict plant diseases, main objective this study is suggest methods that can be used for disease prediction contain raw measurements instead images. A fungus a dataset, which had differences, were chosen so findings could applied future research variety measurements. key difference between two datasets, other than one being plant, dataset balanced only contained classes while imbalanced eighteen classes. All performed well Artificial Network Neighbor best dataset. The types such as fungi, plants, humans, animals.</p>