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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 10, 2023.
Abstract: In recent years, significant advancements have been made in the realm of plant disease classification, with a particular focus on leveraging the capabilities of deep learning techniques. This study delves into the utilization of renowned Convolutional Neural Network (CNN) models, including EfficientNetB5, Mo-bileNet, ResNet50, InceptionV3, and VGG16, for the purpose of plant disease classification. The core methodology involves employing transfer learning, wherein these established CNN models are employed as a foundation and subsequently fine-tuned using a publicly accessible plant disease dataset. The study also compared the results with some deep learning models and with state-of-the-art. Among the tested CNNs, EfficientNetB5 has shown the best performance. EfficientNetB5 has outperformed another model and obtained 99.2% classification accuracy.
Quy Thanh Lu, “An Approach for Classification of Diseases on Leaves” International Journal of Advanced Computer Science and Applications(IJACSA), 14(10), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01410112
@article{Lu2023,
title = {An Approach for Classification of Diseases on Leaves},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01410112},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01410112},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {10},
author = {Quy Thanh Lu}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.