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Multiclass Cucumber Leaf Diseases Recognition Using Best Feature Selection

Nazar Hussain1, Muhammad Attique Khan1, Usman Tariq2, Seifedine Kadry3,*, MuhammadAsfand E. Yar4, Almetwally M. Mostafa5, Abeer Ali Alnuaim6, Shafiq Ahmad7

1 Department of Computer Science, HITEC University Taxila, Taxila, Pakistan
2 College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Khraj, Saudi Arabia
3 Department of Applied Data Science, Noroff University College, Norway
4 Department of Computer Science, Bahria University, Islamabad, Pakistan
5 Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, KSA, Saudi Arabia
6 Department of natural and engineering sciences, College of Applied Studies and Community Service, King Saud University, Riyadh, 11421, Saudi Arabia
7 Faculty of Engineering, Industrial Engineering Department, King Saud University, Riyadh, 11421, Saudi Arabia

* Corresponding Author: Seifedine Kadry. Email: email

(This article belongs to this Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)

Computers, Materials & Continua 2022, 70(2), 3281-3294.


Agriculture is an important research area in the field of visual recognition by computers. Plant diseases affect the quality and yields of agriculture. Early-stage identification of crop disease decreases financial losses and positively impacts crop quality. The manual identification of crop diseases, which are mostly visible on leaves, is a very time-consuming and costly process. In this work, we propose a new framework for the recognition of cucumber leaf diseases. The proposed framework is based on deep learning and involves the fusion and selection of the best features. In the feature extraction phase, VGG (Visual Geometry Group) and Inception V3 deep learning models are considered and fine-tuned. Both fine-tuned models are trained using deep transfer learning. Features are extracted in the later step and fused using a parallel maximum fusion approach. In the later step, best features are selected using Whale Optimization algorithm. The best-selected features are classified using supervised learning algorithms for the final classification process. The experimental process was conducted on a privately collected dataset that consists of five types of cucumber disease and achieved accuracy of 96.5%. A comparison with recent techniques shows the significance of the proposed method.


Cite This Article

N. Hussain, M. Attique Khan, U. Tariq, S. Kadry, M. E. Yar et al., "Multiclass cucumber leaf diseases recognition using best feature selection," Computers, Materials & Continua, vol. 70, no.2, pp. 3281–3294, 2022.


cc 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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