Cover Image

Performance analysis of machine learning algorithms for classification of infection severity levels on rubber leaves

White root disease (WRD) infection in rubber plantations which is caused by Rigidiporus micropores can lead to a significant yield loss. At the early infection stage, it is very difficult to diagnose the disease because infected trees do not exhibit any symptoms. Thus, this study was carried out to...

Full description

Saved in:
Bibliographic Details
Main Authors: Mat Lazim, Siti Saripa Rabiah, Sulaiman, Zulkefly, Mat Nawi, Nazmi, Mohd Mustafah, Anas
Format: Book Section
Language:English
Published: IEEE 2023
Online Access:http://psasir.upm.edu.my/id/eprint/111475/1/Performance%20Analysis%20of%20Machine%20Learning%20Algorithms%20for%20Classification%20of%20Infection.pdf
http://psasir.upm.edu.my/id/eprint/111475/
https://ieeexplore.ieee.org/document/10487727/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.upm.eprints.111475
record_format eprints
spelling my.upm.eprints.1114752024-07-29T08:12:54Z http://psasir.upm.edu.my/id/eprint/111475/ Performance analysis of machine learning algorithms for classification of infection severity levels on rubber leaves Mat Lazim, Siti Saripa Rabiah Sulaiman, Zulkefly Mat Nawi, Nazmi Mohd Mustafah, Anas White root disease (WRD) infection in rubber plantations which is caused by Rigidiporus micropores can lead to a significant yield loss. At the early infection stage, it is very difficult to diagnose the disease because infected trees do not exhibit any symptoms. Thus, this study was carried out to investigate the potential application of spectroscopic technology and machine learning algorithms to classify severity level of infected trees at early stage based on spectral data. A total of 50 leaf samples were used in this work, representing five severity levels; healthy, light, moderate, severe and very severe infection. A visible shortwave near-infrared (VSNIR) spectrometer was used to record the spectral data of the leaf samples. The chlorophyll content of each leaf was measured using SPAD meter. Four classification algorithms investigated in this study were artificial neural network (ANN), support vector machine (SVM), knearest neighbour (kNN) and random forest (RF). The result of the study demonstrates good classification accuracy of 90, 82, 78, and 72% for ANN, SVM, kNN and RF, respectively. This work shows that the spectroscopic measurement combined with classification techniques are promising strategy to classify severity level of WRD based on the spectral data of the rubber leaves. IEEE 2023 Book Section PeerReviewed text en http://psasir.upm.edu.my/id/eprint/111475/1/Performance%20Analysis%20of%20Machine%20Learning%20Algorithms%20for%20Classification%20of%20Infection.pdf Mat Lazim, Siti Saripa Rabiah and Sulaiman, Zulkefly and Mat Nawi, Nazmi and Mohd Mustafah, Anas (2023) Performance analysis of machine learning algorithms for classification of infection severity levels on rubber leaves. In: 2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE). IEEE, Nadi, Fiji, pp. 790-795. ISBN 9798350341072 https://ieeexplore.ieee.org/document/10487727/ 10.1109/csde59766.2023.10487727
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description White root disease (WRD) infection in rubber plantations which is caused by Rigidiporus micropores can lead to a significant yield loss. At the early infection stage, it is very difficult to diagnose the disease because infected trees do not exhibit any symptoms. Thus, this study was carried out to investigate the potential application of spectroscopic technology and machine learning algorithms to classify severity level of infected trees at early stage based on spectral data. A total of 50 leaf samples were used in this work, representing five severity levels; healthy, light, moderate, severe and very severe infection. A visible shortwave near-infrared (VSNIR) spectrometer was used to record the spectral data of the leaf samples. The chlorophyll content of each leaf was measured using SPAD meter. Four classification algorithms investigated in this study were artificial neural network (ANN), support vector machine (SVM), knearest neighbour (kNN) and random forest (RF). The result of the study demonstrates good classification accuracy of 90, 82, 78, and 72% for ANN, SVM, kNN and RF, respectively. This work shows that the spectroscopic measurement combined with classification techniques are promising strategy to classify severity level of WRD based on the spectral data of the rubber leaves.
format Book Section
author Mat Lazim, Siti Saripa Rabiah
Sulaiman, Zulkefly
Mat Nawi, Nazmi
Mohd Mustafah, Anas
spellingShingle Mat Lazim, Siti Saripa Rabiah
Sulaiman, Zulkefly
Mat Nawi, Nazmi
Mohd Mustafah, Anas
Performance analysis of machine learning algorithms for classification of infection severity levels on rubber leaves
author_facet Mat Lazim, Siti Saripa Rabiah
Sulaiman, Zulkefly
Mat Nawi, Nazmi
Mohd Mustafah, Anas
author_sort Mat Lazim, Siti Saripa Rabiah
title Performance analysis of machine learning algorithms for classification of infection severity levels on rubber leaves
title_short Performance analysis of machine learning algorithms for classification of infection severity levels on rubber leaves
title_full Performance analysis of machine learning algorithms for classification of infection severity levels on rubber leaves
title_fullStr Performance analysis of machine learning algorithms for classification of infection severity levels on rubber leaves
title_full_unstemmed Performance analysis of machine learning algorithms for classification of infection severity levels on rubber leaves
title_sort performance analysis of machine learning algorithms for classification of infection severity levels on rubber leaves
publisher IEEE
publishDate 2023
url http://psasir.upm.edu.my/id/eprint/111475/1/Performance%20Analysis%20of%20Machine%20Learning%20Algorithms%20for%20Classification%20of%20Infection.pdf
http://psasir.upm.edu.my/id/eprint/111475/
https://ieeexplore.ieee.org/document/10487727/
_version_ 1806415621869535232
score 13.211869

Similar Items