Open AccessIdentification of Grape Leaf Diseases Using Convolutional Neural Network
Author(s) -
Moh. Arie Hasan,
Dwiza Riana,
Sigit Swasono,
Ade Priyatna,
Eni Pudjiarti,
Lusa Indah Prahartiwi
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1641/1/012007
Subject(s) - convolutional neural network , computer science , python (programming language) , artificial intelligence , deep learning , identification (biology) , machine learning , artificial neural network , pattern recognition (psychology) , botany , biology , operating system
The presence of leaf diseases in grapes can reduce the productivity of grapes and result in losses for farmers. Leaf diseases are mainly caused by bacteria, fungi, virus etc. A proper diagnosis of disease in plants is needed in order to take appropriate control measures. This paper aims to assist in the identification and classification of grape leaf diseases Convolutional Neural Network (CNN). CNN is basically an artificial neural network architecture that requires repeated training processes to get good accuracy. CNN consists of 3 stages, namely Data Input, Feature Learning, and Classification. The implementation of CNN in this study uses Keras libraries that use the python programming language. Keras is a framework created to facilitate learning of computers. The CNN training process using 0.0001 learning rate obtained results with an accuracy rate of 91,37%