Implementation of Transfer Learning in the Convolutional Neural Network Algorithm for Identification of Potato Leaf Disease
Implementasi Transfer Learning pada Algoritma Convolutional Neural Network Untuk Identifikasi Penyakit Daun Kentang
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(1) * Abdul Jalil Rozaqi
University of Amikom Yogyakarta
Indonesia
(2) Muhammad Rudyanto Arief Universitas Amikom Yogyakarta
Indonesia
(3) Andi Sunyoto Universitas Amikom Yogyakarta
Indonesia
(*) Corresponding Author
Abstract
Potatoes are a plant that has many benefits for human life. The potato plant has a problem, namely a disease that attacks the leaves. Disease on potato leaves that is often encountered is early blight and late blight. Image processing is a method that can be used to assist farmers in identifying potato leaf disease by utilizing leaf images. Image processing method development has been done a lot, one of which is by using the Convolutional Neural Network (CNN) algorithm. The CNN method is a good image classification algorithm because its layer architecture can extract leaf image features in depth, however, determining a good CNN architectural model requires a lot of data. CNN architecture will become overfitting if it uses less data, where the classification model has high accuracy on training data but the accuracy becomes poor on test data or new data. This research utilizes the Transfer Learning method to avoid an overfit model when the data used is not ideal or too little. Transfer Learning is a method that uses the CNN architecture that has been trained by other data previously which is then used for image classification on the new data. The purpose of this research was to use the Transfer Learning method on CNN architecture to classify potato leaf images in identifying potato leaf disease. This research compares the Transfer Learning method used to find the best method. The results of the experiments in this research indicate that the Transfer Learning VGG-16 method has the best classification performance results, this method produces the highest accuracy value of 95%.
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References
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[2] K. A. Beals, “Potatoes , Nutrition and Health,” Am. J. Potato Res., vol. 96, pp. 102–110, 2019, doi: 10.1007/s12230-018-09705-4.
[3] J. Chen, J. Chen, D. Zhang, Y. Sun, and Y. A. Nanehkaran, “Using deep transfer learning for image-based plant disease identification,” Comput. Electron. Agric., vol. 173, no. March, p. 105393, 2020, doi: 10.1016/j.compag.2020.105393.
[4] R. Yusianto, Marimin, Suprihatin, and H. Hardjomidjojo, “An Interpretive Structural Modeling (ISM) approach for Institutional Development in the Central Java Potato Agroindustry,” Proc. - 2019 Int. Semin. Appl. Technol. Inf. Commun. Ind. 4.0 Retrosp. Prospect. Challenges, iSemantic 2019, no. 2018, pp. 282–287, 2019, doi: 10.1109/ISEMANTIC.2019.8884325.
[5] S. Tafesse et al., “Farmers’ knowledge and practices of potato disease management in Ethiopia,” NJAS - Wageningen J. Life Sci., vol. 86–87, no. September 2017, pp. 25–38, 2018, doi: 10.1016/j.njas.2018.03.004.
[6] I. K. Abuley and B. J. Nielsen, “Evaluation of models to control potato early blight (Alternaria solani) in Denmark,” Crop Prot., vol. 102, pp. 118–128, 2017, doi: 10.1016/j.cropro.2017.08.012.
[7] B. Kumbar et al., “Field application of Bacillus subtilis isolates for controlling late blight disease of potato caused by Phytophthora infestans,” Biocatal. Agric. Biotechnol., vol. 22, no. April, p. 101366, 2019, doi: 10.1016/j.bcab.2019.101366.
[8] P. Patil, N. Yaligar, and S. . Meena, “Comparision of Performance of Classifiers - SVM, RF and ANN in Potato Blight Disease Detection Using Leaf Images,” IEEE Int. Conf. Comput. Intell. Comput. Res. ICCIC 2017, pp. 1–5, 2017, doi: 10.1109/ICCIC.2017.8524301.
[9] U. Suttapakti and A. Bunpeng, “Potato Leaf Disease Classification Based on Distinct Color and Texture Feature Extraction,” Proc. - 2019 19th Int. Symp. Commun. Inf. Technol. Isc. 2019, no. Mcd, pp. 82–85, 2019, doi: 10.1109/ISCIT.2019.8905128.
[10] M. A. Iqbal and K. H. Talukder, “Detection of Potato Disease Using Image Segmentation and Machine Learning,” 2020 Int. Conf. Wirel. Commun. Signal Process. Networking, WiSPNET 2020, pp. 43–47, 2020, doi: 10.1109/WiSPNET48689.2020.9198563.
[11] F. S. Ni’mah, T. Sutojo, and D. R. I. M. Setiadi, “Identifikasi Tumbuhan Obat Herbal Berdasarkan Citra Daun Menggunakan Algoritma Gray Level Co-occurence Matrix dan K-Nearest Neighbor,” J. Teknol. dan Sist. Komput., vol. 6, no. 2, pp. 51–56, 2018, doi: 10.14710/jtsiskom.6.2.2018.51-56.
[12] P. U. Rakhmawati, Y. M. Pranoto, and E. Setyati, “Klasifikasi Penyakit Daun Kentang Berdasarkan Fitur Tekstur Dan Fitur Warna Menggunakan Support Vector Machine,” Semin. Nas. Teknol. dan Rekayasa 2018, pp. 1–8, 2018.
[13] T. T. Mim, M. H. Sheikh, R. A. Shampa, M. S. Reza, and M. S. Islam, “Leaves Diseases Detection of Tomato Using Image Processing,” in 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART), Nov. 2019, pp. 244–249, doi: 10.1109/SMART46866.2019.9117437.
[14] E. N. Arrofiqoh and H. Harintaka, “Implementasi Metode Convolutional Neural Network Untuk Klasifikasi Tanaman Pada Citra Resolusi Tinggi,” J. Ilm. Geomatika-JIG, vol. 24, no. 2, p. 61, 2018, doi: 10.24895/jig.2018.24-2.810.
[15] Y. D. Zhang et al., “Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation,” Multimed. Tools Appl., vol. 78, no. 3, pp. 3613–3632, 2019, doi: 10.1007/s11042-017-5243-3.
[16] N. Jmour, S. Zayen, and A. Abdelkrim, “Convolutional neural networks for image classification,” 2018 Int. Conf. Adv. Syst. Electr. Technol. IC_ASET 2018, pp. 397–402, 2018, doi: 10.1109/ASET.2018.8379889.
[17] F. F. Maulana and N. Rochmawati, “Klasifikasi Citra Buah Menggunakan Convolutional Neural Network,” J. Informatics Comput. Sci., vol. 01, no. 02, pp. 104–108, 2019, [Online]. Available: jurnalmahasiswa.unesa.ac.id › article.
[18] R. Sharma, A. Singh, and V. Sharma, “Potato Leaf Diseases Identification using CNN,” J. Emerg. Technol. Innov. Res., vol. 5, no. 12, pp. 519–527, 2018.
[19] A. Lumini and L. Nanni, “Deep learning and transfer learning features for plankton classification,” Ecol. Inform., vol. 51, no. November 2018, pp. 33–43, 2019, doi: 10.1016/j.ecoinf.2019.02.007.
[20] T. Kaur and T. K. Gandhi, “Automated brain image classification based on VGG-16 and transfer learning,” Proc. - 2019 Int. Conf. Inf. Technol. ICIT 2019, pp. 94–98, 2019, doi: 10.1109/ICIT48102.2019.00023.
[21] X. Xia, C. Xu, and B. Nan, “Inception-v3 for flower classification,” 2017 2nd Int. Conf. Image, Vis. Comput. ICIVC 2017, pp. 783–787, 2017, doi: 10.1109/ICIVC.2017.7984661.
[22] Z. Zahisham, C. P. Lee, and K. M. Lim, “Food Recognition with ResNet-50,” pp. 1–5, 2020, doi: 10.1109/iicaiet49801.2020.9257825.
[23] B. Wu, Z. Liu, Z. Yuan, G. Sun, and C. Wu, “Reducing overfitting in deep convolutional neural networks using redundancy regularizer,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 10614 LNCS, pp. 49–55, 2017, doi: 10.1007/978-3-319-68612-7_6.
[24] T. O. Emmanuel, “PlantVillage Dataset,” Kaggle.com, 2018. https://www.kaggle.com/emmarex/plantdisease (accessed Jul. 24, 2020).
[25] M. Sokolova and G. Lapalme, “A systematic analysis of performance measures for classification tasks,” Inf. Process. Manag., vol. 45, no. 4, pp. 427–437, 2009, doi: 10.1016/j.ipm.2009.03.002.
[26] S. Ruuska, W. Hämäläinen, S. Kajava, M. Mughal, P. Matilainen, and J. Mononen, “Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle,” Behav. Processes, vol. 148, pp. 56–62, Mar. 2018, doi: 10.1016/j.beproc.2018.01.004.
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Published
2021-04-13
How to Cite
[1]
Abdul Jalil Rozaqi, M. R. Arief, and A. Sunyoto, “Implementation of Transfer Learning in the Convolutional Neural Network Algorithm for Identification of Potato Leaf Disease”, PELS, vol. 1, no. 1, Apr. 2021.
Section
Electrical Engineering
