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

Irfan Haider1, Muhammad Attique Khan1,*, 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: 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

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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APA Style
Haider, I., Khan, M.A., Nazir, M., Kim, T., Cha, J. (2024). An artificial intelligence-based framework for fruits disease recognition using deep learning. Computer Systems Science and Engineering, 48(2), 529-554. https://doi.org/10.32604/csse.2023.042080
Vancouver Style
Haider I, Khan MA, Nazir M, Kim T, Cha J. An artificial intelligence-based framework for fruits disease recognition using deep learning. Comput Syst Sci Eng. 2024;48(2):529-554 https://doi.org/10.32604/csse.2023.042080
IEEE Style
I. Haider, M.A. Khan, M. Nazir, T. Kim, and J. Cha "An Artificial Intelligence-Based Framework for Fruits Disease Recognition Using Deep Learning," Comput. Syst. Sci. Eng., vol. 48, no. 2, pp. 529-554. 2024. https://doi.org/10.32604/csse.2023.042080



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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