Open AccessDetection of Disease and Pest of Kenaf Plant using Convolutional Neural Network
Author(s) -
Diny Melsye Nurul Fajri,
Wayan Firdaus Mahmudy,
Titiek Yulianti
Publication year - 2021
Publication title -
jitecs (journal of information technology and computer science)
Language(s) - English
Resource type - Journals
eISSN - 2540-9824
pISSN - 2540-9433
DOI - 10.25126/jitecs.202161195
Subject(s) - kenaf , convolutional neural network , yield (engineering) , environmentally friendly , computer science , agricultural engineering , fiber , pulp and paper industry , artificial intelligence , engineering , biology , materials science , composite material , ecology
Kenaf fiber is mainly used for forest wood substitute industrial products. Thus, the kenaf fiber can be promoted as the main composition of environmentally friendly goods. Unfortunately, there are several Kenaf gardens that have been stricken with the disease-causing a lack of yield. By utilizing advances in technology, it was felt to be able to help kenaf farmers quickly and accurately detect which pests or diseases attacked their crops. This paper will discuss the application of the machine learning method which is a Convolutional Neural Network (CNN) that can provide results for inputting leaf images into the results of temporary diagnoses. The data used are 838 image data for 4 classes. The average results prove that with CNN an accuracy value of 73% can be achieved for the detection of diseases and plant pests in Kenaf plants.