Open AccessClassification Of Rice Plant Diseases Using the Convolutional Neural Network Method
Author(s) -
A A Je Veggy Priyangka,
I Made Surya Kumara
Publication year - 2021
Publication title -
lontar komputer/lontar komputer
Language(s) - English
Resource type - Journals
eISSN - 2541-5832
pISSN - 2088-1541
DOI - 10.24843/lkjiti.2021.v12.i02.p06
Subject(s) - convolutional neural network , rice plant , production (economics) , agriculture , artificial intelligence , population , computer science , agricultural engineering , artificial neural network , test data , machine learning , pattern recognition (psychology) , agronomy , geography , engineering , biology , medicine , environmental health , economics , archaeology , programming language , macroeconomics
Indonesia is one of the countries with the population majority of farming. The agricultural sector in Indonesia is supported by fertile land and a tropical climate. Rice is one of the agricultural sectors in Indonesia. Rice production in Indonesia has decreased every year. Thus, rice production factors are very significant. Rice disease is one of the factors causing the decline in rice production in Indonesia. Technological developments have made it easier to recognize the types of rice plant diseases. Machine learning is one of the technologies used to identify types of rice diseases. The classification system of rice plant disease used the Convolutional Neural Network method. Convolutional Neural Network (CNN) is a machine learning method used in object recognition. This method applies to the VGG19 architecture, which has features to improve results. The image used as training and test data consists of 105 images, divided into training and test images. Parameter testing using epoch variations and data augmentation. The research results obtained a test accuracy of 95.24%.