Disease Classification on Rice Leaves using DenseNet121, DenseNet169, DenseNet201
Keywords:Rice Leaf Disease Detection, DenseNet121, DenseNet169, DenseNet201, Machine Learning, Deep Learning Training
Rice is a plant that can grow in the tropics. This plant can produce food that can meet the needs of the people of a country. This plant can grow well if it is cared for properly. If the planting has used good care, such as providing adequate water, adding good fertilizer, it can be ascertained that it will produce a lot of rice fruit after harvesting. This often causes concern if rice growers have given good care but often produce less rice fruit because rice plants are attacked by various diseases. This is what makes the problem, that rice plants are attacked by diseases. Before spraying diseases or pests, farmers should have an understanding of diseases in rice. This makes farmers not wrong in choosing drugs for farmers' rice. It is very vulnerable if farmers do not know about the rice disease. Therefore, it is necessary to observe what types of rice diseases attack rice plants. Observations are not enough just to take pictures with a camera. But it is necessary to carry out further analysis of rice diseases. The presence of information technology is now able to recognize any type. One of the machine learning technologies is able to detect rice diseases. One of these branches of machine learning is deep learning. By using a dataset that focuses on rice disease, the model generated from deep learning training is able to detect rice disease. The purpose of this research is to predict disease in rice leaves using deep learning, namely DenseNet. Training using DenseNet, namely DenseNet121, DenseNet169 and DenseNet201. Accuracy using DenseNet121 reached 91.67%, DenseNet169 reached 90%, and DenseNet201 reached 88.33%. The model training time takes 24 seconds.
Bohmrah, M. K., & Kaur, H. (2021). Classification of Covid-19 patients using efficient Fine-tuned Deep learning DenseNet Model. Global Transitions Proceedings, 0–14. https://doi.org/10.1016/j.gltp.2021.08.003
El-Ateif, S., & Idri, A. (2022). Single-modality and joint fusion deep learning for diabetic retinopathy diagnosis. Scientific African, 17, e01280. https://doi.org/10.1016/j.sciaf.2022.e01280
Faizin, A., Arsanto, A. T., Musa, A. R., Informatika, P. T., Pasuruan, U. Y., & Blight, B. (2022). DEEP PRE-TRAINED MODEL MENGGUNAKAN ARSITEKTUR DENSENET. 6(2), 615–621.
Fjellström, C., & Nyström, K. (2022). Deep learning, stochastic gradient descent and diffusion maps. Journal of Computational Mathematics and Data Science, 4(June), 100054. https://doi.org/10.1016/j.jcmds.2022.100054
Goodfellow, I., Mehdi, M., Bing, X., & David, W. (2014). Generative Adversarial Nets.
Hindarto, D. (2022). Perbandingan Kinerja Akurasi Klasifikasi K-NN, NB dan DT pada APK Android. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 9(1), 486–503. https://doi.org/10.35957/jatisi.v9i1.1542
Hindarto, D., & Handri Santoso. (2021). Android APK Identification using Non Neural Network and Neural Network Classifier. Journal of Computer Science and Informatics Engineering (J-Cosine), 5(2), 149–157. https://doi.org/10.29303/jcosine.v5i2.420
Hindarto, D., Indrajit, R. E., & Dazki, E. (2021). Sustainability of Implementing Enterprise Architecture in the Solar Power Generation Manufacturing Industry. Sinkron, 6(1), 13–24. https://jurnal.polgan.ac.id/index.php/sinkron/article/view/11115
Hindarto, D., & Santoso, H. (2022). PERFORMANCE COMPARISON OF SUPERVISED LEARNING USING NON-NEURAL NETWORK AND NEURAL NETWORK. Janapati, 11, 49–62.
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-Janua, 2261–2269. https://doi.org/10.1109/CVPR.2017.243
Kingma, D. P., & Ba, J. L. (2015). Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 1–15.
Kotsyuba, I., Shikov, A., Romanov, K., Galperin, M., Kudriashov, A., & Zhukova, T. (2022). The development of the recommendatory resource for the adaptive learning of transport philosophy. Transportation Research Procedia, 63, 600–606. https://doi.org/10.1016/j.trpro.2022.06.053
LAHSAINI, I., EL HABIB DAHO, M., & CHIKH, M. A. (2021). Deep transfer learning based classification model for covid-19 using chest CT-scans. Pattern Recognition Letters, 152, 122–128. https://doi.org/10.1016/j.patrec.2021.08.035
Li, Q., Xiong, D., & Shang, M. (2022). Adjusted stochastic gradient descent for latent factor analysis. Information Sciences, 588, 196–213. https://doi.org/10.1016/j.ins.2021.12.065
Maharjan, J., Calvert, J., Pellegrini, E., Green-Saxena, A., Hoffman, J., McCoy, A., Mao, Q., & Das, R. (2021). Application of deep learning to identify COVID-19 infection in posteroanterior chest X-rays. Clinical Imaging, 80, 268–273. https://doi.org/10.1016/j.clinimag.2021.07.004
Muhammad Hammad Saleem, Johan Potgieter, K. M. A. (2016). Plant Disease Detection and Classification by Deep Learning. Nature, 29(7553), 1–73. http://deeplearning.net/
Pepper, N., Crespo, L., & Montomoli, F. (2022). Adaptive learning for reliability analysis using Support Vector Machines. Reliability Engineering and System Safety, 226(June), 108635. https://doi.org/10.1016/j.ress.2022.108635
Sze, E., Santoso, H., & Hindarto, D. (2022). Review Star Hotels Using Convolutional Neural Network. 7(1), 2469–2477.
Xu, Z., Ma, W., Lin, P., & Hua, Y. (2022). Deep learning of rock microscopic images for intelligent lithology identification: Neural network comparison and selection. Journal of Rock Mechanics and Geotechnical Engineering, 14(4), 1140–1152. https://doi.org/10.1016/j.jrmge.2022.05.009
How to Cite
Copyright (c) 2023 Adi Dwifana Saputra, Djarot Hindarto, Handri Santoso
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.