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DOI: 10.14569/IJACSA.2022.0131011
PDF

Evaluation of the Efficiency of the Optimization Algorithms for Transfer Learning on the Rice Leaf Disease Dataset

Author 1: Luyl-Da Quach
Author 2: Khang Nguyen Quoc
Author 3: Anh Nguyen Quynh
Author 4: Hoang Tran Ngoc

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 10, 2022.

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Abstract: To improve the model's efficiency, people use many different methods, including the Transfer Learning algorithm, to improve the efficiency of recognition and classification of image data. The study was carried out to combine optimization algorithms with the Transfer Learning model with MobileNet, MobileNetV2, InceptionV3, Xception, ResNet50V2, DenseNet201 models. Then, testing on rice disease data set with 13.186 images, background removed. The result obtained with high accuracy is the RMSprop algorithm, with an accuracy of 88% when combined with the Xception model, similar to the F1, Xception model, and ResNet50V2 score of 87% when combined with the Adam algorithm. This shows the effect of gradients on the Transition learning model. Research, evaluate and draw the optimal model to build a website to identify diseases on rice leaves, with the main functions including images and recording of disease identification points for better management purposes on diseased areas of rice.

Keywords: Optimization algorithm; transfer learning; RMSprop; rice leaf disease; Adam

Luyl-Da Quach, Khang Nguyen Quoc, Anh Nguyen Quynh and Hoang Tran Ngoc, “Evaluation of the Efficiency of the Optimization Algorithms for Transfer Learning on the Rice Leaf Disease Dataset” International Journal of Advanced Computer Science and Applications(IJACSA), 13(10), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131011

@article{Quach2022,
title = {Evaluation of the Efficiency of the Optimization Algorithms for Transfer Learning on the Rice Leaf Disease Dataset},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131011},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131011},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {10},
author = {Luyl-Da Quach and Khang Nguyen Quoc and Anh Nguyen Quynh and Hoang Tran Ngoc}
}



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.

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