An Approach for Classification of Diseases on Leaves

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}
}