Open Access

ARTICLE

Crops Leaf Diseases Recognition: A Framework of Optimum Deep Learning Features

Shafaq Abbas¹, Muhammad Attique Khan¹, Majed Alhaisoni², Usman Tariq³, Ammar Armghan⁴, Fayadh Alenezi⁴, Arnab Majumdar⁵, Orawit Thinnukool^6,*

1 Department of Computer Science, HITEC University, Taxila, Pakistan
2 Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
3 College of Computer Engineering and Science, Prince Sattam Bin Abdulaziz University, Al-Kharaj, 11942, Saudi Arabia
4 Department of Electrical Engineering, College of Engineering, Jouf University, Sakakah, Saudi Arabia
5 Faculty of Engineering, Imperial College London, London, SW7 2AZ, UK
6 College of Arts, Media, and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand

* Corresponding Author: Orawit Thinnukool. Email:

Computers, Materials & Continua 2023, 74(1), 1139-1159. https://doi.org/10.32604/cmc.2023.028824

Received 18 February 2022; Accepted 04 May 2022; Issue published 22 September 2022

Abstract

Manual diagnosis of crops diseases is not an easy process; thus, a computerized method is widely used. From a couple of years, advancements in the domain of machine learning, such as deep learning, have shown substantial success. However, they still faced some challenges such as similarity in disease symptoms and irrelevant features extraction. In this article, we proposed a new deep learning architecture with optimization algorithm for cucumber and potato leaf diseases recognition. The proposed architecture consists of five steps. In the first step, data augmentation is performed to increase the numbers of training samples. In the second step, pre-trained DarkNet19 deep model is opted and fine-tuned that later utilized for the training of fine-tuned model through transfer learning. Deep features are extracted from the global pooling layer in the next step that is refined using Improved Cuckoo search algorithm. The best selected features are finally classified using machine learning classifiers such as SVM, and named a few more for final classification results. The proposed architecture is tested using publicly available datasets–Cucumber National Dataset and Plant Village. The proposed architecture achieved an accuracy of 100.0%, 92.9%, and 99.2%, respectively. A comparison with recent techniques is also performed, revealing that the proposed method achieved improved accuracy while consuming less computational time.

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Keywords

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