Open Access


Plant Disease Diagnosis and Image Classification Using Deep Learning

Rahul Sharma1, Amar Singh1, Kavita2, N. Z. Jhanjhi3, Mehedi Masud4, Emad Sami Jaha5, Sahil Verma2,*
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.

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


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.


Plant diseases detection; CNN; image classification; deep learning in agriculture

Cite This Article

R. Sharma, A. Singh, K. , N. Z. Jhanjhi, M. Masud et al., "Plant disease diagnosis and image classification using deep learning," Computers, Materials & Continua, vol. 71, no.2, pp. 2125–2140, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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