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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika
Saputra, Danang Eka
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Computer Science & IT

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Rice Plants Disease Identification Using Deep Learning with Convolutional Neural Network Method Jatmika, Sunu; Saputra, Danang Eka
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11540

Abstract

Indonesia is an agricultural country where most of the population grows rice and most farmers cannot detect early if there is a pest attack on rice plants . This research discuss about deep learning implementation to classify or identify diseases in rice leaves using mobile application. This system will make users easily to diagnose diseases by displaying diagnostic results in the form of the name of the disease along with its taxonomy, disease description and drug recommendations for disease solutions. There are four classes of leaves used in this research, including healthy leaves, leaf blight, brown spot and potassium deficiency. The design of the model uses two approaches, one of them are modeling convolutional neural network from the scratch and modeling with transfer learning using inception v3 architecture. Both models will go through training process to produce a model that is ready to be used for classification. In application testing, a comparison is made between two models. From the tests that have been carried out, it is concluded that the system with model made using transfer learning approach, produce good accuracy with an accuracy of 90%. Meanwhile the System with the other model gain an accuracy of 62%. So when the data used in research are extremely low, it is best to use transfer learning as an approach to design a mode.
Co-Authors Sunu Jatmika, Sunu