DOI: https://doi.org/10.14710/jtsiskom.2021.14013

Klasifikasi penyakit citra daun anggur menggunakan model CNN-VGG16

Grape leaf image disease classification using CNN-VGG16 model

Department of Computer Science, Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri. Jalan Kramat Raya No. 18, Senen, Jakarta Pusat, DKI Jakarta 10450, Indonesia

Received: 17 Dec 2020; Revised: 26 Jun 2021; Accepted: 5 Jul 2021; Published: 31 Oct 2021.
Open Access Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
This study aims to classify the disease image on grape leaves using image processing. The segmentation uses the k-means clustering algorithm, the feature extraction process uses the VGG16 transfer learning technique, and the classification uses CNN. The dataset is from Kaggle of 4000 grape leaf images for four classes: leaves with black measles, leaf spot, healthy leaf, and blight. Google images of 100 pieces were also used as test data outside the dataset. The accuracy of the CNN model training is 99.50 %. The classification yields an accuracy of 97.25 % using the test data, while using test image data outside the dataset obtains an accuracy of 95 %. The designed image processing method can be applied to identify and classify disease images on grape leaves.
Fulltext View|Download Email colleagues
Keywords: k-means clustering; VGG16 transfer learning; grape leaves classification; CNN
Funding: Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri

Article Metrics:

Article Info
Section: Original Research Articles
Language : ID
Most viewed articles
Implementasi Algoritma Kriptografi RSA untuk Enkripsi dan Dekripsi Email Perancangan Perangkat Lunak untuk Analisis Tingkat Fokus pada Citra Mikroskop Digital Menggunakan Proses Ekstraksi Ciri Pengembangan Sistem Pakar Untuk Diagnosis Penyakit Hepatitis Berbasis Web Menggunakan Metode Certainty Factor Perancangan dan Pengembangan Sistem Informasi Perpustakaan Berbasis Web (Studi Kasus Jurusan Sistem Komputer) Decision Support System for Thesis Graduation Recommendation Using AHP-TOPSIS Method More articles
Most cited articles
Decision Support System for Thesis Graduation Recommendation Using AHP-TOPSIS Method Sistem Pengendali Peralatan Elektronik dalam Rumah secara Otomatis Menggunakan Sensor PIR, Sensor LM35, dan Sensor LDR Sistem Monitoring Digital Penggunaan dan Kualitas Kekeruhan Air PDAM Berbasis Mikrokontroler ATMega328 Menggunakan Sensor Aliran Air dan Sensor Fotodiode Pengembangan Sistem Informasi Rekam Medis untuk Dinas Kabupaten Grobogan Performance comparison of RSA and AES to SMS messages compression using Huffman algorithm More cited articles
  1. T. Kurniastuti, “Pengaruh berbagai macam panjang stek terhadap pertumbuhan bibit anggur (Vitis vinivera L.),” Agri-tek: Jurnal Ilmu Pertanian, Kehutanan dan Agroteknologi, vol. 17, no. 1, pp. 1–7, 2016
  2. P. I. Hidayati, “Analisis hama pada tanamana anggur dengan pendekatan metode CF (Certainty Factor) berbasis mobile Android,” SMATIKA Jurnal, vol. 8, no. 1, pp. 9–17, 2018. doi: 10.32664/smatika.v8i01.194
  3. M. Y. Amin, A. Mahmudi, and N. Verdyansyah, “Sistem pakar diagnosis penyakit buah anggur menggunakan metode dempster shafer berbasis web,” JATI (Jurnal Mahasiswa Teknik Informatik), vol. 4, no. 1, pp. 1–7, 2020. doi: 10.36040/jati.v4i1.2385
  4. T. F. Kusumaningrum, “Implementasi convolution neural network (CNN) untuk klasifikasi jamur konsumsi di Indonesia menggunakan Keras,” B.Sc thesis, Universitas Islam Indonesia, Yogyakarta, Indonesia, 2018
  5. W. S. E. Putra, “Klasifikasi citra menggunakan convolutional neural network (CNN) pada Caltech 101,” Jurnal Teknik ITS, vol. 5, no. 1, pp. 65-69, 2016. doi: 10.12962/j23373539.v5i1.15696
  6. M. Zufar, “Convolutional neural networks untuk pengenalan wajah secara real-time,” B.Sc thesis, Institut Teknologi Sepuluh November, Surabaya, Indonesia, 2016
  7. R. Patil, S. Udgave, S. More, D. Nemishte, and M. Kasture, “Grape leaf disease detection using k-means clustering algorithm,” International Research Journal of Engineering and Technology, vol. 3, no. 4, pp. 2330–2333, 2016
  8. P. B. Padol and A. A. Yadav, “SVM classifier based grape leaf disease detection,” in Conference on Advances in Signal Processing, Pune, India, Jun. 2016, pp. 175–179. doi: 10.1109/CASP.2016.7746160
  9. N. Krithika, and A. G. Selvarani, “An individual grape leaf disease identification using leaf skeletons and KNN classification,” in International Conference on Innovations in Information, Embedded and Communication Systems, Coimbatore, India, Mar. 2017, pp. 1-5. doi: 10.1109/ICIIECS.2017.8275951
  10. N. Agrawal and J. Singhai, “Grape leaf disease detection and classification using multi-class support vector machine,” in International Conference on Recent Innovations in Signal processing and Embedded Systems, Bhopal, India, Oct. 2017, pp. 238–244. doi: 10.1109/RISE.2017.8378160
  11. J. Zhu, A. Wu, X. Wang, and H. Zhang, “Identification of grape diseases using image analysis and BP neural networks,” Multimedia Tools and Application, vol. 79, pp. 14539–14551, 2019. doi: 10.1007/s11042-018-7092-0
  12. E. Rezende, G. Ruppert, T. Carvalho, A. Theophilo, F. Ramos, and P. de Geus, “Malicious software classification using VGG16 deep neural network’s bottleneck features,” Advances in Intelligent Systems and Computing, vol. 738, pp. 51–59, 2018. doi: 10.1007/978-3-319-77028-4_9
  13. Z. Liu et al., “Improved kiwifruit detection using pre-trained VGG16 with RGB and NIR information fusion,” IEEE Access, vol. 8, pp. 2327–2336, 2020, doi: 10.1109/ACCESS.2019.2962513
  14. P. Hridayami, I. K. G. D. Putra, and K. S. Wibawa, “Fish species recognition using VGG16 deep convolutional neural network,” Journal of Computing Science and Engineering, vol. 13, no. 3, pp. 124–130, 2019. doi: 10.5626/JCSE.2019.13.3.124
  15. M. G. Sadewo, A. P. Windarto, and D. Hartama, “Penerapan datamining pada populasi daging ayam ras pedaging di Indonesia berdasarkan provinsi menggunakan K-Means clustering,” InfoTekJar (Jurnal Nasional Informasi dan Teknologi Jaringan), vol. 2, no. 1, pp. 60–67, 2017 doi: 10.30743/infotekjar.v2i1.164
  16. B. L. B, X. Zhang, Z. Gao, and L. Chen, “Weld defect images classification with VGG16-based neural network,” in International Forum on Digital TV and Wireless Multimedia Communications, Shanghai, China, Nov. 2017, pp. 215–223. doi: 10.1007/978-981-10-8108-8
  17. D. M. W. Powers, "Evaluation: from precision, recall and f-measure to ROC, informedness, markedness & correlation," Journal of Machine Learning Technologies, vol. 2, no. 1, pp. 37–63, 2011

Last update:

  1. SISTEM DETEKSI MASKER PADA WAJAH MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK ARSITEKTUR VGG16

    Mohammad Ushuludin, Sam Farisa Chaerul Haviana, Imam Much Ibnu Subroto. Transmisi: Jurnal Ilmiah Teknik Elektro, 25 (4), 2023. doi: 10.14710/transmisi.25.4.179-185

  2. An Improvement on Exploration Step of Whale Optimization Algorithm with Levy Distribution for Classification Problems

    Sakkayaphop Pravesjit, Krittika Kantawong, Natdanai Kamkhad, Saksit Sabaiporn, Jantawan Monchanuan, Duangjai Jitkongchuen, Arit Thammano, Panchit Longpradit. 2024 5th International Conference on Big Data Analytics and Practices (IBDAP), 2024. doi: 10.1109/IBDAP62940.2024.10689697

  3. Computer-Aided Detection and Classification of Monkeypox and Chickenpox Lesion in Human Subjects Using Deep Learning Framework

    Dilber Uzun Ozsahin, Mubarak Taiwo Mustapha, Berna Uzun, Basil Duwa, Ilker Ozsahin. Diagnostics, 13 (2), 2023. doi: 10.3390/diagnostics13020292

  4. Multiclass classification of diseased grape leaf identification using deep convolutional neural network(DCNN) classifier

    Kerehalli Vinayaka Prasad, Hanumesh Vaidya, Choudhari Rajashekhar, Kumar Swamy Karekal, Renuka Sali, Kottakkaran Sooppy Nisar. Scientific Reports, 14 (1), 2024. doi: 10.1038/s41598-024-59562-x

  5. Modification of Sand Cat Swarm Optimization for Classification Problems

    Sakkayaphop Pravesjit, Krittika Kantawong, Sathien Hunta, Duangjai Jitkongchuen, Arit Thammano, Panchit Longpradit. 2024 5th International Conference on Big Data Analytics and Practices (IBDAP), 2024. doi: 10.1109/IBDAP62940.2024.10689683

  6. GrapeLeafNet: A Dual-Track Feature Fusion Network With Inception-ResNet and Shuffle-Transformer for Accurate Grape Leaf Disease Identification

    R. Karthik, R. Menaka, S. Ompirakash, P. Bala Murugan, M. Meenakashi, Sindhia Lingaswamy, Daehan Won. IEEE Access, 12 , 2024. doi: 10.1109/ACCESS.2024.3361044

  7. Klasifikasi Penyakit Daun Pada Tanaman Jagung Menggunakan Algoritma Support Vector Machine, K-Nearest Neighbors dan Multilayer Perceptron

    Jaka Kusuma, Rubianto, Rika Rosnelly, Hartono, B. Herawan Hayadi. Journal of Applied Computer Science and Technology, 4 (1), 2023. doi: 10.52158/jacost.v4i1.484

Last update: 2024-11-02 07:17:04

No citation recorded.