Open Access

ARTICLE

Plant Disease Diagnosis and Image Classification Using Deep Learning

by Rahul Sharma¹, Amar Singh¹, None Kavita², N. Z. Jhanjhi³, Mehedi Masud⁴, Emad Sami Jaha⁵, Sahil Verma^2,*

1 School of Computer Applications, Lovely Professional University Jalandhar, 144411, India
2 Department of Computer Science and Engineering, Chandigarh University, Mohali, India
3 Department of Computer Science and Engineering, Tailor's University, Malaysia
4 Department of Computer Science, College of Computer and Information Technology, Taif University, Taif, 21944, Saudi Arabia
5 Department of Computer Science, Faculty of Computer and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

* Corresponding Author: Sahil Verma. Email:

Computers, Materials & Continua 2022, 71(2), 2125-2140. https://doi.org/10.32604/cmc.2022.020017

Received 06 May 2021; Accepted 09 July 2021; Issue published 07 December 2021

Abstract

Indian agriculture is striving to achieve sustainable intensification, the system aiming to increase agricultural yield per unit area without harming natural resources and the ecosystem. Modern farming employs technology to improve productivity. Early and accurate analysis and diagnosis of plant disease is very helpful in reducing plant diseases and improving plant health and food crop productivity. Plant disease experts are not available in remote areas thus there is a requirement of automatic low-cost, approachable and reliable solutions to identify the plant diseases without the laboratory inspection and expert's opinion. Deep learning-based computer vision techniques like Convolutional Neural Network (CNN) and traditional machine learning-based image classification approaches are being applied to identify plant diseases. In this paper, the CNN model is proposed for the classification of rice and potato plant leaf diseases. Rice leaves are diagnosed with bacterial blight, blast, brown spot and tungro diseases. Potato leaf images are classified into three classes: healthy leaves, early blight and late blight diseases. Rice leaf dataset with 5932 images and 1500 potato leaf images are used in the study. The proposed CNN model was able to learn hidden patterns from the raw images and classify rice images with 99.58% accuracy and potato leaves with 97.66% accuracy. The results demonstrate that the proposed CNN model performed better when compared with other machine learning image classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree and Random Forest.

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Keywords

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