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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.

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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.
Keywords: k-means clustering; VGG16 transfer learning; grape leaves classification; CNN
Funding: Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri

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