Analyzing InceptionV3 and InceptionResNetV2 with Data Augmentation for Rice Leaf Disease Classification
DOI:
https://doi.org/10.62411/faith.2024-4Keywords:
Data augmentation impact, Image classification, Image recognition, Rice leaf disease classification, Transfer learningAbstract
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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United States Department of Agriculture, "Grain: World Markets and Trade," 2023. https://public.govdelivery.com/accounts/USDAFAS/subscriber/new (accessed Dec. 20, 2023).
E. B. Wijayanti, D. R. I. M. Setiadi, and B. H. Setyoko, "Dataset Analysis and Feature Characteristics to Predict Rice Production based on eXtreme Gradient Boosting," J. Comput. Theor. Appl., vol. 1, no. 3, pp. 299–310, Feb. 2024, doi: 10.62411/jcta.10057.
Y. Lu, S. Yi, N. Zeng, Y. Liu, and Y. Zhang, "Identification of rice diseases using deep convolutional neural networks," Neurocomputing, vol. 267, pp. 378–384, Dec. 2017, doi: 10.1016/j.neucom.2017.06.023.
M. Jayaram, G. Kalpana, S. R. Borra, and B. D. Bhavani, "A brief study on rice diseases recognition and image classification: fusion deep belief network and S-particle swarm optimization algorithm," Int. J. Electr. Comput. Eng., vol. 13, no. 6, p. 6302, Dec. 2023, doi: 10.11591/ijece.v13i6.pp6302-6311.
T. R. Noviandy, K. Nisa, G. M. Idroes, I. Hardi, and N. R. Sasmita, "Classifying Beta-Secretase 1 Inhibitor Activity for Alzheimer's Drug Discovery with LightGBM," J. Comput. Theor. Appl., vol. 1, no. 4, pp. 358–367, Mar. 2024, doi: 10.62411/jcta.10129.
S. B. Imanulloh, A. R. Muslikh, and D. R. I. M. Setiadi, "Plant Diseases Classification based Leaves Image using Convolutional Neural Network," J. Comput. Theor. Appl., vol. 1, no. 1, pp. 1–10, Aug. 2023, doi: 10.33633/jcta.v1i1.8877.
R. K. Rachman, D. R. I. M. Setiadi, A. Susanto, K. Nugroho, and H. M. M. Islam, "Enhanced Vision Transformer and Transfer Learning Approach to Improve Rice Disease Recognition," J. Comput. Theor. Appl., vol. 1, no. 4, pp. 446–460, Apr. 2024, doi: 10.62411/jcta.10459.
S. Ali, A. Hashmi, A. Hamza, U. Hayat, and H. Younis, "Dynamic and Static Handwriting Assessment in Parkinson's Disease: A Synergistic Approach with C-Bi-GRU and VGG19," J. Comput. Theor. Appl., vol. 1, no. 2, pp. 151–162, Dec. 2023, doi: 10.33633/jcta.v1i2.9469.
A. Junaidi, D. Qi, C. W. Howe, and S. Z. M. Hashim, "Enhancing Rice Leaf Disease Classification: A Combined Algorithm Approach for Improved Accuracy and Robustness," in Proceedings of the 4th International Conference on Electronics, Biomedical Engineering, and Health Informatics, 2024, pp. 185–203. doi: 10.1007/978-981-97-1463-6_13.
H. T. Adityawan, O. Farroq, S. Santosa, H. M. M. Islam, M. K. Sarker, and D. R. I. M. Setiadi, "Butterflies Recognition using Enhanced Transfer Learning and Data Augmentation," J. Comput. Theor. Appl., vol. 1, no. 2, pp. 115–128, Nov. 2023, doi: 10.33633/jcta.v1i2.9443.
M. T. H. Khan Tusar, M. T. Islam, A. H. Sakil, M. N. H. N. Khandaker, and M. M. Hossain, "An Intelligent Telediagnosis of Acute Lymphoblastic Leukemia using Histopathological Deep Learning," J. Comput. Theor. Appl., vol. 2, no. 1, pp. 1–12, May 2024, doi: 10.62411/jcta.10358.
M. S. Sunarjo, H. Gan, and D. R. I. M. Setiadi, "High-Performance Convolutional Neural Network Model to Identify COVID-19 in Medical Images," J. Comput. Theor. Appl., vol. 1, no. 1, pp. 19–30, Aug. 2023, doi: 10.33633/jcta.v1i1.8936.
M. T. Ahad, Y. Li, B. Song, and T. Bhuiyan, "Comparison of CNN-based deep learning architectures for rice diseases classification," Artif. Intell. Agric., vol. 9, pp. 22–35, Jul. 2023, doi: 10.1016/j.aiia.2023.07.001.
A. R. Muslikh, D. R. I. M. Setiadi, and A. A. Ojugo, "Rice Disease Recognition using Transfer Learning Xception Convolutional Neural Network," J. Tek. Inform., vol. 4, no. 6, pp. 1535–1540, Dec. 2023, doi: 10.52436/1.jutif.2023.4.6.1529.
V. K. Shrivastava, M. K. Pradhan, S. Minz, and M. P. Thakur, "Rice plant disease classification using transfer learning of deep convolution neural network," Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., vol. 42, no. 3/W6, pp. 631–635, 2019, doi: 10.5194/isprs-archives-XLII-3-W6-631-2019.
K. N, L. V. Narasimha Prasad, C. S. Pavan Kumar, B. Subedi, H. B. Abraha, and V. E. Sathishkumar, "Rice leaf diseases prediction using deep neural networks with transfer learning," Environ. Res., vol. 198, no. May, p. 111275, 2021, doi: 10.1016/j.envres.2021.111275.
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning." arXiv, Feb. 23, 2016. [Online]. Available: http://arxiv.org/abs/1602.07261
S. Ghosal and K. Sarkar, "Rice Leaf Diseases Classification Using CNN With Transfer Learning," in 2020 IEEE Calcutta Conference (CALCON), Feb. 2020, pp. 230–236. doi: 10.1109/CALCON49167.2020.9106423.
D. Jiang, F. Li, Y. Yang, and S. Yu, "A Tomato Leaf Diseases Classification Method Based on Deep Learning," in 2020 Chinese Control And Decision Conference (CCDC), Aug. 2020, pp. 1446–1450. doi: 10.1109/CCDC49329.2020.9164457.
Ü. Atila, M. Uçar, K. Akyol, and E. Uçar, "Plant leaf disease classification using EfficientNet deep learning model," Ecol. Inform., vol. 61, p. 101182, Mar. 2021, doi: 10.1016/j.ecoinf.2020.101182.
C. Szegedy, V. Vanhoucke, J. Shlens, and Z. Wojna, "Rethinking the Inception Architecture for Computer Vision." arXiv, 2015. [Online]. Available: https://arxiv.org/abs/1512.00567
P. K. Sethy, N. K. Barpanda, A. K. Rath, and S. K. Behera, "Deep feature based rice leaf disease identification using support vector machine," Comput. Electron. Agric., vol. 175, no. May, p. 105527, 2020, doi: 10.1016/j.compag.2020.105527.
A. Mikolajczyk and M. Grochowski, "Data augmentation for improving deep learning in image classification problem," in 2018 International Interdisciplinary PhD Workshop (IIPhDW), May 2018, pp. 117–122. doi: 10.1109/IIPHDW.2018.8388338.
Q. Zheng, M. Yang, X. Tian, N. Jiang, and D. Wang, "A Full Stage Data Augmentation Method in Deep Convolutional Neural Network for Natural Image Classification," Discret. Dyn. Nat. Soc., vol. 2020, pp. 1–11, Jan. 2020, doi: 10.1155/2020/4706576.
O. Jaiyeoba, E. Ogbuju, O. T. Yomi, and F. Oladipo, "Development of a Model to Classify Skin Diseases using Stacking Ensemble Machine Learning Techniques," J. Comput. Theor. Appl., vol. 2, no. 1, pp. 22–38, May 2024, doi: 10.62411/jcta.10488.
F. S. Gomiasti, W. Warto, E. Kartikadarma, J. Gondohanindijo, and D. R. I. M. Setiadi, "Enhancing Lung Cancer Classification Effectiveness Through Hyperparameter-Tuned Support Vector Machine," J. Comput. Theor. Appl., vol. 1, no. 4, pp. 396–406, Mar. 2024, doi: 10.62411/jcta.10106.
M. Çiftçi, M. U. Türkdamar, and C. Öztürk, "Leveraging YOLO Models for Safety Equipment Detection on Construction Sites," J. Comput. Theor. Appl., vol. 1, no. 4, pp. 492–506, May 2024, doi: 10.62411/jcta.10453.
M. Aggarwal et al., "Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets," Agronomy, vol. 13, no. 10, p. 2483, Sep. 2023, doi: 10.3390/agronomy13102483.
G. Kathiresan, M. Anirudh, M. Nagharjun, and R. Karthik, "Disease detection in rice leaves using transfer learning techniques," J. Phys. Conf. Ser., vol. 1911, no. 1, p. 012004, May 2021, doi: 10.1088/1742-6596/1911/1/012004.
J. Chen, D. Zhang, Y. A. Nanehkaran, and D. Li, "Detection of rice plant diseases based on deep transfer learning," J. Sci. Food Agric., vol. 100, no. 7, pp. 3246–3256, May 2020, doi: 10.1002/jsfa.10365.
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