Open Access

ARTICLE

Citrus Diseases Recognition Using Deep Improved Genetic Algorithm

Usra Yasmeen¹, Muhammad Attique Khan¹, Usman Tariq², Junaid Ali Khan¹, Muhammad Asfand E. Yar³, Ch. Avais Hanif⁴, Senghour Mey⁵, Yunyoung Nam^6,*

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 Computer Science, Bahria University, Islamabad, Pakistan
4 Department of EE, HITEC University Taxila, Taxila, Pakistan
5 Department of ICT Convergence, Soonchunhyang University, Korea
6 Department of Computer Science and Engineering, Soonchunhyang University, Korea

* Corresponding Author: Yunyoung Nam. Email:

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

Computers, Materials & Continua 2022, 71(2), 3667-3684. https://doi.org/10.32604/cmc.2022.022264

Received 02 August 2021; Accepted 30 September 2021; Issue published 07 December 2021

Abstract

Agriculture is the backbone of each country, and almost 50% of the population is directly involved in farming. In Pakistan, several kinds of fruits are produced and exported the other countries. Citrus is an important fruit, and its production in Pakistan is higher than the other fruits. However, the diseases of citrus fruits such as canker, citrus scab, blight, and a few more impact the quality and quantity of this Fruit. The manual diagnosis of these diseases required an expert person who is always a time-consuming and costly procedure. In the agriculture sector, deep learning showing significant success in the last five years. This research work proposes an automated framework using deep learning and best feature selection for citrus diseases classification. In the proposed framework, the augmentation technique is applied initially by creating more training data from existing samples. They were then modifying the two pre-trained models named Resnet18 and Inception V3. The modified models are trained using an augmented dataset through transfer learning. Features are extracted for each model, which is further selected using Improved Genetic Algorithm (ImGA). The selected features of both models are fused using an array-based approach that is finally classified using supervised learning classifiers such as Support Vector Machine (SVM) and name a few more. The experimental process is conducted on three different datasets-Citrus Hybrid, Citrus Leaf, and Citrus Fruits. On these datasets, the best-achieved accuracy is 99.5%, 94%, and 97.7%, respectively. The proposed framework is evaluated on each step and compared with some recent techniques, showing that the proposed method shows improved performance.

---

Keywords

---

Citations