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

Mohd Suhaimi Sulaiman, Zuraidi Saad


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. 


Classification; White root disease; Artificial neural network; Levenberg marquardt; Scaled conjugate gradient

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