Open Access

ARTICLE

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

Samra Rehman¹, Muhammad Attique Khan¹, Majed Alhaisoni², Ammar Armghan³, Usman Tariq⁴, Fayadh Alenezi³, Ye Jin Kim⁵, Byoungchol Chang^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 Department of Electrical Engineering, College of Engineering, Jouf University, Sakakah, Saudi Arabia
4 Department of Management Information Systems, CoBA, Prince Sattam bin Abdulaziz University, Saudi Arabia
5 Department of Computer Science, Hanyang University, Seoul, 04763, Korea
6 Center for Computational Social Science, Hanyang University, Seoul, 04763, Korea

* Corresponding Author: Byoungchol Chang. Email:

Computers, Materials & Continua 2023, 75(1), 697-714. https://doi.org/10.32604/cmc.2023.035183

Received 10 August 2022; Accepted 12 November 2022; Issue published 06 February 2023

Abstract

Identifying fruit disease manually is time-consuming, expert-required, and expensive; thus, a computer-based automated system is widely required. Fruit diseases affect not only the quality but also the quantity. As a result, it is possible to detect the disease early on and cure the fruits using computer-based techniques. However, computer-based methods face several challenges, including low contrast, a lack of dataset for training a model, and inappropriate feature extraction for final classification. In this paper, we proposed an automated framework for detecting apple fruit leaf diseases using CNN and a hybrid optimization algorithm. Data augmentation is performed initially to balance the selected apple dataset. After that, two pre-trained deep models are fine-tuning and trained using transfer learning. Then, a fusion technique is proposed named Parallel Correlation Threshold (PCT). The fused feature vector is optimized in the next step using a hybrid optimization algorithm. The selected features are finally classified using machine learning algorithms. Four different experiments have been carried out on the augmented Plant Village dataset and yielded the best accuracy of 99.8%. The accuracy of the proposed framework is also compared to that of several neural nets, and it outperforms them all.

---

Keywords

---