Open Access

ARTICLE

A Cascaded Design of Best Features Selection for Fruit Diseases Recognition

by Faiz Ali Shah¹, Muhammad Attique Khan², Muhammad Sharif¹, Usman Tariq³, Aimal Khan⁴, Seifedine Kadry⁵, Orawit Thinnukool^6,*

1 Department of Computer Science, COMSATS University Islamabad, Wah Campus, 47080, Pakistan
2 Department of Computer Science, HITEC University Taxila, Taxila, 47040, Pakistan
3 College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Khraj, Saudi Arabia
4 National University of Science and Technology Islamabad, Islamabad, Pakistan
5 Faculty of Applied Computing and Technology, Noroff University College, Kristiansand, Norway
6 Research Group of Embedded Systems and Mobile Application in Health Science, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand

* Corresponding Author: Orawit Thinnukool. Email:

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

Computers, Materials & Continua 2022, 70(1), 1491-1507. https://doi.org/10.32604/cmc.2022.019490

Received 15 April 2021; Accepted 05 June 2021; Issue published 07 September 2021

Abstract

Fruit diseases seriously affect the production of the agricultural sector, which builds financial pressure on the country's economy. The manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an expert. Hence, it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf images. According to the literature, many automated methods have been developed for the recognition of fruit diseases at the early stage. However, these techniques still face some challenges, such as the similar symptoms of different fruit diseases and the selection of irrelevant features. Image processing and deep learning techniques have been extremely successful in the last decade, but there is still room for improvement due to these challenges. Therefore, we propose a novel computerised approach in this work using deep learning and featuring an ant colony optimisation (ACO) based selection. The proposed method consists of four fundamental steps: data augmentation to solve the imbalanced dataset, fine-tuned pre-trained deep learning models (NasNet Mobile and MobileNet-V2), the fusion of extracted deep features using matrix length, and finally, a selection of the best features using a hybrid ACO and a Neighbourhood Component Analysis (NCA). The best-selected features were eventually passed to many classifiers for final recognition. The experimental process involved an augmented dataset and achieved an average accuracy of 99.7%. Comparison with existing techniques showed that the proposed method was effective.

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