Classification of healthy and white root disease infected rubber trees based on relative permittivity and capacitance input properties using LM and SCG artificial neural network

• Published in: Indonesian Journal of Electrical Engineering and Computer Science
• Main Author: Sulaiman M.S.; Saad Z.
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2020
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083108145&doi=10.11591%2fijeecs.v19.i1.pp222-228&partnerID=40&md5=85fffa07d32ee3f7f7ab8592d2117d56

White root disease is one of the most serious diseases in rubber plantation in Malaysia that originally infects on the root surface of the rubber tree. So, prevention is important compared to treatment. The classification system proposed in the research had the ability of detecting the disease by classifying between healthy rubber trees and white root disease infected rubber trees. 600 samples of latex from healthy rubber trees and white root disease infected rubber trees were taken from the RRIM station in Kota Tinggi, Johor. These samples were measured based on its relative permittivity and capacitance. All of the measurement inputs from the experiment were tested using statistical analysis. These measurement input were then went through the process of classification in ANN to generate the optimized models by using LM and SCG algorithm. There were four optimized models selected from the classification process. The accuracy from the selected most optimized models were greater than 70%. The selected most optimized models were then used to classify between healthy trees and white root infected trees based on single input categories. Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved.