Open AccessOptimization of the CNN model for smart agriculture
Author(s) -
. Gunawan,
Muhammad Zarlis,
Poltak Sihombing,
Sutarman Sutarman
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1088/1/012029
Subject(s) - convolutional neural network , computer science , artificial intelligence , palm oil , focus (optics) , image processing , machine learning , deep learning , pattern recognition (psychology) , image (mathematics) , computer vision , agroforestry , physics , optics , biology
Artificial intelligence is a branch of computer science that has advanced quite rapidly and is very useful in helping to alleviate human tasks. One of the models or algorithms part of artificial intelligence is the Convolutional Neural Network (CNN). This model is proven to be very accurate in image data processing. CNN has been used for facial recognition and image data detection systems. In this study, researchers will optimize CNN to obtain a faster and more accurate model for processing plant disease image data in this study using oil palm plant sample data. With the Research and Development method and FGD (Focus Group Discussion) with various experts, this research is expected to produce a CNN model that is faster and more accurate.