Open Access

ARTICLE

An Artificial Intelligence-Based Framework for Fruits Disease Recognition Using Deep Learning

by Irfan Haider¹, Muhammad Attique Khan^1,*, Muhammad Nazir¹, Taerang Kim², Jae-Hyuk Cha²

1 Department of Computer Science, HITEC University, Taxila, 47080, Pakistan
2 Department of Computer Science, Hanyang University, Seoul, Korea

* Corresponding Author: Muhammad Attique Khan. Email:

(This article belongs to the Special Issue: Vision Transformer and Next-Generation AI Tools for Smart Surveillance, Healthcare, and Agriculture Systems)

Computer Systems Science and Engineering 2024, 48(2), 529-554. https://doi.org/10.32604/csse.2023.042080

Received 17 May 2023; Accepted 13 July 2023; Issue published 19 March 2024

Abstract

Fruit infections have an impact on both the yield and the quality of the crop. As a result, an automated recognition system for fruit leaf diseases is important. In artificial intelligence (AI) applications, especially in agriculture, deep learning shows promising disease detection and classification results. The recent AI-based techniques have a few challenges for fruit disease recognition, such as low-resolution images, small datasets for learning models, and irrelevant feature extraction. This work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder optimization. Three fruit types have been employed in this work for the validation process, such as apple, grape, and Citrus. In the first step, a noisy dataset is prepared by employing the original images to learn the designed framework better. The EfficientNet-B0 deep model is fine-tuned on the next step and trained separately on the original and noisy data. After that, features are fused using a serial concatenation approach that is later optimized in the next step using an improved Path Finder Algorithm (PFA). This algorithm aims to select the best features based on the fitness score and ignore redundant information. The selected features are finally classified using machine learning classifiers such as Medium Neural Network, Wide Neural Network, and Support Vector Machine. The experimental process was conducted on each fruit dataset separately and obtained an accuracy of 100%, 99.7%, 99.7%, and 93.4% for apple, grape, Citrus fruit, and citrus plant leaves, respectively. A detailed analysis is conducted and also compared with the recent techniques, and the proposed framework shows improved accuracy.

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Keywords

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