Open AccessGrape leaf image disease classification using CNN-VGG16 model
Author(s) -
Moh. Arie Hasan,
Yan Riyanto,
Dwiza Riana
Publication year - 2021
Publication title -
jurnal teknologi dan sistem komputer
Language(s) - English
Resource type - Journals
eISSN - 2620-4002
pISSN - 2338-0403
DOI - 10.14710/jtsiskom.2021.14013
Subject(s) - artificial intelligence , pattern recognition (psychology) , computer science , cluster analysis , image processing , feature extraction , image segmentation , contextual image classification , segmentation , image (mathematics)
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.