Issue

Vol. 8, No. 1, February 2023

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The Implementation of Pretrained VGG16 Model for Rice Leaf Disease Classification using Image Segmentation

https://doi.org/10.22219/kinetik.v8i1.1592
Jody Ririt Krido Suseno
Universitas Muhammadiyah Malang
Yufis Azhar
Universitas Muhammadiyah Malang
Agus Eko Minarno
Universitas Muhammadiyah Malang

Corresponding Author(s) : Agus Eko Minarno

aguseko@umm.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 8, No. 1, February 2023

Abstract

Rice is an agricultural sector that produces rice which is one of the staple foods for the majority of the population in Indonesia. In the cultivation of rice plants there are also factors that affect rice production and are not realized by farmers causing that they are late in handling and diagnosing symptoms and making rice production decline. Therefore, it is necessary to have an early diagnosis of rice plants to identify them correctly, quickly and accurately. Machine learning is one of the classification techniques to detect various plant diseases such as rice plants. There are several studies on machine learning using the Convolutional Neural Network with the VGG16 model to classify rice leaf diseases and using Image Segmentation techniques on rice leaf datasets for make the image becomes a form that is not too complicated to analyze. The data used in this research is Rice Leaf Disease which consists of 3 classes including Bacterial leaf blight, Brown spot, and Leaf smut. Then segmentation is carried out using two techniques, namely threshold and k means. Then data augmentation for make dataset used has a large and varied number and training using VGG16 model with hyperparameter tuning and obtained 91.66% accuracy results for scenarios with the k-means dataset.

Keywords

Image Segmentation Classification Convolutional Neural Network VGG16 Rice Leaf Disease
Suseno, J. R. K., Azhar, Y., & Minarno, A. E. (2023). The Implementation of Pretrained VGG16 Model for Rice Leaf Disease Classification using Image Segmentation. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 8(1), 499-506. https://doi.org/10.22219/kinetik.v8i1.1592
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. I. N. Azizah, P. R. Arum, and R. Wasono, “Model terbaik uji multikolinearitas untuk analisis faktor-faktor yang mempengaruhi produksi padi di Kabupaten Blora tahun 2020,” Pros. Semin. Nas. UNIMUS, vol. 4, pp. 61–69, 2021.
  2. A. Purnamawati, W. Nugroho, D. Putri, and W. F. Hidayat, “Deteksi Penyakit Daun pada Tanaman Padi Menggunakan Algoritma Decision Tree, Random Forest, Naïve Bayes, SVM dan KNN,” InfoTekJar J. Nas. Inform. dan Teknol. Jar. Attrib. 4.0 Int., vol. 5, no. 1, 2020. https://doi.org/10.30743/infotekjar.v5i1.2934
  3. C. R. Rahman et al., “Identification and recognition of rice diseases and pests using convolutional neural networks,” Biosyst. Eng., vol. 194, pp. 112–120, 2020. https://doi.org/10.1016/j.biosystemseng.2020.03.020
  4. P. Mekha and N. Teeyasuksaet, “Image Classification of Rice Leaf Diseases Using Random Forest Algorithm,” 2021 Jt. 6th Int. Conf. Digit. Arts, Media Technol. with 4th ECTI North. Sect. Conf. Electr. Electron. Comput. Telecommun. Eng. ECTI DAMT NCON 2021, pp. 165–169, 2021. https://doi.org/10.1109/ECTIDAMTNCON51128.2021.9425696
  5. R. Wadhawan, M. Garg, and A. K. Sahani, “Rice Plant Leaf Disease Detection and Severity Estimation,” 2020 IEEE 15th Int. Conf. Ind. Inf. Syst. ICIIS 2020 - Proc., no. 978, pp. 455–459, 2020. https://doi.org/10.1109/ICIIS51140.2020.9342653
  6. K. Sudha Rani and B. Priya Madhuri, “Plant Leaf Disease Detection Using Machine Learning Techniques,” Lect. Notes Data Eng. Commun. Technol., vol. 58, pp. 511–518, 2021. https://doi.org/10.1007/978-981-15-9647-6_40
  7. M. M. H. Matin, A. Khatun, M. G. Moazzam, and M. S. Uddin, “An Efficient Disease Detection Technique of Rice Leaf Using AlexNet,” J. Comput. Commun., vol. 08, no. 12, pp. 49–57, 2020. https://doi.org/10.4236/jcc.2020.812005
  8. 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. April, p. 111275, 2021. https://doi.org/10.1016/j.envres.2021.111275
  9. M. Subramanian, N. P. Narasimha, J. B., M. B. A., and S. Ve, “Hyperparameter Optimization for Transfer Learning of VGG16 for Disease Identification in Corn Leaves Using Bayesian Optimization,” Big Data, vol. 10, no. 3, pp. 215–229, 2022. https://doi.org/10.1089/big.2021.0218
  10. S. M. Hassan, A. K. Maji, M. Jasiński, Z. Leonowicz, and E. Jasińska, “Identification of plant-leaf diseases using cnn and transfer-learning approach,” Electron., vol. 10, no. 12, 2021. https://doi.org/10.3390/electronics10121388
  11. V. K. Shrivastava, M. K. Pradhan, and M. P. Thakur, “Application of Pre-Trained Deep Convolutional Neural Networks for Rice Plant Disease Classification,” Int. Conf. Artif. Intell. Smart Syst., pp. 1023–1030, 2021. https://doi.org/10.1109/ICAIS50930.2021.9395813
  12. S. Ghosal and K. Sarkar, “Rice Leaf Diseases Classification Using CNN with Transfer Learning,” 2020 IEEE Calcutta Conf. CALCON 2020 - Proc., pp. 230–236, 2020. https://doi.org/10.1109/CALCON49167.2020.9106423
  13. M. A. Rahman, M. M. Islam, G. M. S. Mahdee, and M. W. Ul Kabir, “Improved Segmentation Approach for Plant Disease Detection,” 1st Int. Conf. Adv. Sci. Eng. Robot. Technol. 2019, ICASERT 2019, vol. 2019, no. Icasert, pp. 1–5, 2019. https://doi.org/10.1109/ICASERT.2019.8934895
  14. H. B. Prajapati, J. P. Shah, and V. K. Dabhi, “Detection and classification of rice plant diseases using image processing techniques,” Intell. Decis. Technol., vol. 11, no. 3, pp. 357–373, 2017. https://doi.org/10.3233/IDT-170301
  15. M. E. Pothen and D. M. L. Pai, “Detection of Rice Leaf Diseases Using Image Processing,” Proc. 4th Int. Conf. Comput. Methodol. Commun. ICCMC 2020, no. Iccmc, pp. 424–430, 2020. https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00080
  16. A. S. Nasution, A. Alvin, A. T. Siregar, and M. S. Sinaga, “KNN Algorithm for Identification of Tomato Disease Based on Image Segmentation Using Enhanced K-Means Clustering,” Kinetik. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 4, no. 3, 2022. https://doi.org/10.22219/kinetik.v7i3.1486
  17. S. R. Karanam, Y. Srinivas, and M. V. Krishna, “Study on image processing using deep learning techniques,” Mater. Today Proc., no. xxxx, 2020. https://doi.org/10.1016/j.matpr.2020.09.536
  18. N. Ganatra and A. Patel, “A multiclass plant leaf disease detection using image processing and machine learning techniques,” Int. J. Emerg. Technol., vol. 11, no. 2, pp. 1082–1086, 2020.
  19. R. Jain, P. Nagrath, G. Kataria, V. Sirish Kaushik, and D. Jude Hemanth, “Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning,” Meas. J. Int. Meas. Confed., vol. 165, p. 108046, 2020. https://doi.org/10.1016/j.measurement.2020.108046
  20. K. S. Vepuri, “Improving Facial Emotion Recognition with Image processing and Deep Learning,” Int. J. Comput. …, 2021, [Online]. Available: https://scholarworks.sjsu.edu/etd_projects/1030
  21. Ulfah Nur Oktaviana, Ricky Hendrawan, Alfian Dwi Khoirul Annas, and Galih Wasis Wicaksono, “Klasifikasi Penyakit Padi berdasarkan Citra Daun Menggunakan Model Terlatih Resnet101,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 6, pp. 1216–1222, 2021. https://doi.org/10.29207/resti.v5i6.3607
  22. U. O. Dorj, K. K. Lee, J. Y. Choi, and M. Lee, “The skin cancer classification using deep convolutional neural network,” Multimed. Tools Appl., vol. 77, no. 8, pp. 9909–9924, 2018. https://doi.org/10.1007/s11042-018-5714-1
  23. Y. Tian, “Artificial Intelligence Image Recognition Method Based on Convolutional Neural Network Algorithm,” IEEE Access, vol. 8, pp. 125731–125744, 2020. https://doi.org/10.1109/ACCESS.2020.3006097
  24. K. Z. Thet, K. K. Htwe, and M. M. Thein, “Grape Leaf Diseases Classification using Convolutional Neural Network,” Proc. 4th Int. Conf. Adv. Inf. Technol. ICAIT 2020, pp. 147–152, 2020. https://doi.org/10.1109/ICAIT51105.2020.9261801
  25. S. Ramesh and D. Vydeki, “Application of machine learning in detection of blast disease in south indian rice crops,” J. Phytol., vol. 11, pp. 31–37, 2019. https://doi.org/10.25081/jp.2019.v11.5476
  26. M. Koklu, I. Cinar, and Y. S. Taspinar, “Classication of rice varieties with deep learning methods,” Dep. Comput. Eng. Selcuk Univ. Konya, Turkey, vol. 187, 2021. https://doi.org/10.1016/j.compag.2021.106285
Read More
Author biographies is not available.
Download this PDF file
PDF
Statistic
Read Counter : 25 Download : 23

Downloads

Download data is not yet available.