Open Access

ARTICLE

Deep Learning Based Automated Detection of Diseases from Apple Leaf Images

Swati Singh¹, Isha Gupta², Sheifali Gupta², Deepika Koundal^3,*, Sultan Aljahdali⁴, Shubham Mahajan⁵, Amit Kant Pandit⁵

1 Department of Electronics and Communication Engineering, University Institute of Technology, Himachal Pradesh University, Shimla, 171005, India
2 Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
3 Department of Systemics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
4 Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
5 School of Electronics & Communication, Shri Mata Vaishno Devi University, Katra, 185320, India

* Corresponding Author: Deepika Koundal. Email:

(This article belongs to the Special Issue: Applications of Intelligent Systems in Computer Vision)

Computers, Materials & Continua 2022, 71(1), 1849-1866. https://doi.org/10.32604/cmc.2022.021875

Received 18 July 2021; Accepted 30 August 2021; Issue published 03 November 2021

Abstract

In Agriculture Sciences, detection of diseases is one of the most challenging tasks. The mis-interpretations of plant diseases often lead to wrong pesticide selection, resulting in damage of crops. Hence, the automatic recognition of the diseases at earlier stages is important as well as economical for better quality and quantity of fruits. Computer aided detection (CAD) has proven as a supportive tool for disease detection and classification, thus allowing the identification of diseases and reducing the rate of degradation of fruit quality. In this research work, a model based on convolutional neural network with 19 convolutional layers has been proposed for effective and accurate classification of Marsonina Coronaria and Apple Scab diseases from apple leaves. For this, a database of 50,000 images has been acquired by collecting images of leaves from apple farms of Himachal Pradesh (H.P) and Uttarakhand (India). An augmentation technique has been performed on the dataset to increase the number of images for increasing the accuracy. The performance analysis of the proposed model has been compared with the new two Convolutional Neural Network (CNN) models having 8 and 9 layers respectively. The proposed model has also been compared with the standard machine learning classifiers like support vector machine, k-Nearest Neighbour, Random Forest and Logistic Regression models. From experimental results, it has been observed that the proposed model has outperformed the other CNN based models and machine learning models with an accuracy of 99.2%.

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