SVM Classifier based Disease Detection in Plant Leaf using CNN and Hybrid Features

  • Ashutosh Kumar Singh, Dr. Bharti Chourasia, Dr. Neetesh Raghuwanshi

Abstract

Plant diseases are an unfavourable factor that causes a significant decrease in the quality and quantity of crops. Experienced biologists or farmers often observe plants with the naked eye for disease, but this method is often imprecise and can take a long time. In this study, we use artificial intelligence and computer vision techniques to achieve the goal of designing and developing an intelligent classification mechanism for leaf diseases. In this paper, data augmentation is performed on the PlantVillage dataset images (for apple, corn, potato, tomato and rice plants) and their deep features are extracted using Convolutional Neural Network (CNN). The pre-processing of dataset images and their texture and color features are extracted by Histogram of Oriented Gradient (HoG), GLCM and color moments. Here the three types of features, i.e. color, texture and deep features are combined to form hybrid features. These hybrid features are classified by Support Vector Machine Classifier to get the simulation results. The comparative analysis of proposed techniques is presented with the use of evaluation parameters; precision, sensitivity, f-score and accuracy.

Published
2021-11-22
How to Cite
Ashutosh Kumar Singh, Dr. Bharti Chourasia, Dr. Neetesh Raghuwanshi. (2021). SVM Classifier based Disease Detection in Plant Leaf using CNN and Hybrid Features. Design Engineering, 14618 - 14637. Retrieved from http://www.thedesignengineering.com/index.php/DE/article/view/6588
Section
Articles