Open Access

ARTICLE

Leaf Blights Detection and Classification in Large Scale Applications

by Abdul Muiz Fayyaz¹, Kawther A. Al-Dhlan², Saeed Ur Rehman¹, Mudassar Raza¹, Waqar Mehmood³, Muhammad Shafiq⁴, Jin-Ghoo Choi^4,*

1 Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
2 Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha'il, Hail, Saudi Arabia
3 Department of IT and Computer Science, PAF-Institute of Applied Sciences and Technology, Mang, Haripur, Pakistan
4 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea

* Corresponding Author: Jin-Ghoo Choi. Email:

(This article belongs to the Special Issue: Soft Computing Methods for Innovative Software Practices)

Intelligent Automation & Soft Computing 2022, 31(1), 507-522. https://doi.org/10.32604/iasc.2022.016392

Received 30 December 2020; Accepted 02 June 2021; Issue published 03 September 2021

Abstract

Crops are very important to the financial needs of a country. Due to various diseases caused by different pathogens, a large number of crops have been destroyed. As humanoids, our basic need is food for survival, and the most basic foundation of our food is agriculture. For many developing countries, it is mainly an important source of income. Bacterial diseases are one of the main diseases that cause improper production and a major economic crisis for the country. Therefore, it is necessary to detect the disease early. However, it is not easy for humans to analyze the different leaves of plants by themselves when recognizing diseases. In this article, a variety of machine learning methods are used to classify and detect leaf blight. We use the fusion of deep convolutional neural network (CNN) models obtained from SqueezeNet and ShuffleNet to improve the accuracy and robustness of large-scale applications. We use entropy to reduce the complexity of the calculation process and reduce the features in the deep learning process. In addition, we use a support vector machine (SVM) classifier to obtain the classification. We use the CIELAB color space to capture the entire color range to improve accuracy. Our results are very promising because we have achieved 98% accuracy in the early detection of leaf blight.

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Keywords

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