Mathematics and Computer Science, Volume 2

4Detection and Classification of Leaf Blast Disease using Decision Tree Algorithm in Rice Crop

Sarvesh Vishwakarma^1⋆ and Bhavna Chilwal²

¹Department of CSE, Graphic Era (Deemed to be University), Dehradun, India

²Department of CSE, DIT University Dehradun, Uttarakhand, India

Abstract

The agricultural field is the most important field for any nation but some issues prevail and affect agricultural products every year. Agricultural diseases are the main concern for yield loss. This chapter uses the Decision Tree technique to form a tree structure for leaf blast disease level detection in rice crops. A Decision Tree is used as a classification technique and here disease levels are classified based on symptoms that occur during infection. The Iterative Dichotomiser 3 (ID3) algorithm is one of the important methods to form a Decision Tree based on entropy and information gain. The nodes in the tree are the symptoms that have different labels for disease occurrence. This decision tree will help detect the occurrence of disease as per the symptoms and help farmers get information about the severity level of a disease so that they can take required measures on time to save the crop from loss.

Keywords: Decision tree, ID3 algorithm, entropy, information gain, leaf blast disease

4.1 Introduction

There are so many fungal diseases that prevail in rice, but this chapter focuses on leaf blast disease in rice crops. Blast disease is also known as rice fever. Agricultural scientists find out ...