Analyzing InceptionV3 and InceptionResNetV2 with Data Augmentation for Rice Leaf Disease Classification

Authors

DOI:

https://doi.org/10.62411/faith.2024-4

Keywords:

Data augmentation impact, Image classification, Image recognition, Rice leaf disease classification, Transfer learning

Abstract

This research aims to evaluate and compare the performance of several deep learning architectures, especially InceptionV3 and InceptionResNetV2, with other models, such as EfficientNetB3, ResNet50, and VGG19, in classifying rice leaf diseases. In addition, this research also evaluates the impact of using data augmentation on model performance. Three different datasets were used in this experiment, varying the number of images and class distribution. The results show that InceptionV3 and InceptionResNetV2 consistently perform excellently and accurately on most datasets. Data augmentation has varying effects, providing slight advantages on datasets with lower variation. The findings from this research are that the InceptionV3 model is the best model for classifying rice diseases based on leaf images. The InceptionV3 model produces accuracies of 99.53, 58.94, and 90.00 for datasets 1, 2, and 3, respectively. It is also necessary to be wise in carrying out data augmentation by considering the dataset's characteristics to ensure the resulting model can generalize well.

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Author Biographies

Fadel Muhamad Firnando, Dian Nuswantoro University

Faculty of Computer Science, Dian Nuswantoro University, Semarang, Indonesia

De Rosal Ignatius Moses Setiadi, Dian Nuswantoro University

Faculty of Computer Science, Dian Nuswantoro University, Semarang, Indonesia

Ahmad Rofiqul Muslikh, Universitas Merdeka Malang

Faculty of Information Technology, Universitas Merdeka Malang, Indonesia

Syahroni Wahyu Iriananda, Universitas Widya Gama Malang

Department of Informatics Engineering, Universitas Widya Gama Malang, Indonesia

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Published

2024-05-21

How to Cite

[1]
F. M. Firnando, D. R. I. M. Setiadi, A. R. Muslikh, and S. W. Iriananda, “Analyzing InceptionV3 and InceptionResNetV2 with Data Augmentation for Rice Leaf Disease Classification”, J. Fut. Artif. Intell. Tech., vol. 1, no. 1, pp. 1–11, May 2024.

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