Detection of Aglaonema Ornamental Plant Diseases Using Convolutional Neural Network Method (Case Study: As Florist).

Abstract

Scale is a type of disease caused by the presence of mites on the underside of leaves, multiplying by consuming vital fluids in Aglaonema. Diseases in Aglaonema leaves can be caused by various factors, including pathogenic microorganisms, environmental disturbances, or other factors such as care mistakes. This research aims to detect diseases in Aglaonema leaves using several stages and processes. The first stage involves converting RGB images, followed by feature extraction using convolutional neural network methods to separate areas of diseased and healthy leaves. The obtained results are then used to classify the types of diseases using Convolutional Neural Network (CNN) methods. The research findings indicate that the system is capable of identifying disease types with an accuracy rate of up to 80% with a dataset of 100 images tested on 20 images.