Internet of things-based soil sensing platform for Ganoderma boninense infection detection in oil palm seedlings using machine learning

Basal stem rot (BSR), caused by a white-rot fungus Ganoderma boninense is a destructive disease that causes tremendous losses in the oil palm industry. The primary route of the disease infection is through root that has contact with Ganoderma boninense inoculum in the soil. The use of planting mater...

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Main Author:	Abdul Aziz, Mohd Hamim
Format:	Thesis
Language:	English
Published:	2021
Subjects:	Internet of things - Industrial applications Machine learning Ganoderma diseases of plants
Online Access:	http://psasir.upm.edu.my/id/eprint/104208/1/MOHD%20HAMIM%20BIN%20ABDUL%20AZIZ%20-%20IR.pdf
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id	my-upm-ir.104208
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spelling	my-upm-ir.1042082023-07-25T01:09:21Z Internet of things-based soil sensing platform for Ganoderma boninense infection detection in oil palm seedlings using machine learning 2021-04 Abdul Aziz, Mohd Hamim Basal stem rot (BSR), caused by a white-rot fungus Ganoderma boninense is a destructive disease that causes tremendous losses in the oil palm industry. The primary route of the disease infection is through root that has contact with Ganoderma boninense inoculum in the soil. The use of planting materials (seedlings) that are resistant to Ganoderma boninense could prevent the spread of BSR disease in the plantation. A manual census is used commonly by nurseries to monitor the progress of the disease development associated with various treatments. This common nursery practice is usually conducted every two to four weeks. An irregular monitoring leads to delays in detecting the disease occurrence. This study, therefore, is focused on the use of a sensor network to obtain soil data to diagnose the Ganoderma boninense infection using the internet of things (IoT) platform. This approach could lead to a possible early infection detection methodology since rapid monitoring can avoid missing data. The objectives of the research include studying the potential use of soil properties as the indicators for BSR disease, analyzing temporal changes of infected seedlings, and developing the Ganoderma boninense disease detection model using soil properties. A total of 40 oil palm seedlings aged five months old were used in the study. They consisted of 20 healthy and 20 infected seedlings. The infected seedlings were prepared by artificially inoculating the tree roots with the Ganoderma boninense rubber woodblock. The seedlings were placed in the greenhouse with controlled environmental temperature and humidity. Three soil sensors were buried at 8 cm depth in each seedling's growth medium to measure the amount of soil moisture content (MC) in volumetric water content (in %), soil electrical conductivity (EC) (in μS/cm), and soil temperature (T) (in °C). The soil parameters data was collected every hour daily for 24 weeks (six months). These data were stored in the cloud (ThingSpeak) and available for real-time monitoring and data extraction for further analysis. The results of soil analysis revealed that more than 80% of monitored weeks in all parameters yielded Internet of things - Industrial applications Machine learning Ganoderma diseases of plants 2021-04 Thesis http://psasir.upm.edu.my/id/eprint/104208/ http://psasir.upm.edu.my/id/eprint/104208/1/MOHD%20HAMIM%20BIN%20ABDUL%20AZIZ%20-%20IR.pdf text en public doctoral Universiti Putra Malaysia Internet of things - Industrial applications Machine learning Ganoderma diseases of plants Bejo, Siti Khairunniza
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
advisor	Bejo, Siti Khairunniza
topic	Internet of things - Industrial applications Machine learning Ganoderma diseases of plants
spellingShingle	Internet of things - Industrial applications Machine learning Ganoderma diseases of plants Abdul Aziz, Mohd Hamim Internet of things-based soil sensing platform for Ganoderma boninense infection detection in oil palm seedlings using machine learning
description	Basal stem rot (BSR), caused by a white-rot fungus Ganoderma boninense is a destructive disease that causes tremendous losses in the oil palm industry. The primary route of the disease infection is through root that has contact with Ganoderma boninense inoculum in the soil. The use of planting materials (seedlings) that are resistant to Ganoderma boninense could prevent the spread of BSR disease in the plantation. A manual census is used commonly by nurseries to monitor the progress of the disease development associated with various treatments. This common nursery practice is usually conducted every two to four weeks. An irregular monitoring leads to delays in detecting the disease occurrence. This study, therefore, is focused on the use of a sensor network to obtain soil data to diagnose the Ganoderma boninense infection using the internet of things (IoT) platform. This approach could lead to a possible early infection detection methodology since rapid monitoring can avoid missing data. The objectives of the research include studying the potential use of soil properties as the indicators for BSR disease, analyzing temporal changes of infected seedlings, and developing the Ganoderma boninense disease detection model using soil properties. A total of 40 oil palm seedlings aged five months old were used in the study. They consisted of 20 healthy and 20 infected seedlings. The infected seedlings were prepared by artificially inoculating the tree roots with the Ganoderma boninense rubber woodblock. The seedlings were placed in the greenhouse with controlled environmental temperature and humidity. Three soil sensors were buried at 8 cm depth in each seedling's growth medium to measure the amount of soil moisture content (MC) in volumetric water content (in %), soil electrical conductivity (EC) (in μS/cm), and soil temperature (T) (in °C). The soil parameters data was collected every hour daily for 24 weeks (six months). These data were stored in the cloud (ThingSpeak) and available for real-time monitoring and data extraction for further analysis. The results of soil analysis revealed that more than 80% of monitored weeks in all parameters yielded
format	Thesis
qualification_level	Doctorate
author	Abdul Aziz, Mohd Hamim
author_facet	Abdul Aziz, Mohd Hamim
author_sort	Abdul Aziz, Mohd Hamim
title	Internet of things-based soil sensing platform for Ganoderma boninense infection detection in oil palm seedlings using machine learning
title_short	Internet of things-based soil sensing platform for Ganoderma boninense infection detection in oil palm seedlings using machine learning
title_full	Internet of things-based soil sensing platform for Ganoderma boninense infection detection in oil palm seedlings using machine learning
title_fullStr	Internet of things-based soil sensing platform for Ganoderma boninense infection detection in oil palm seedlings using machine learning
title_full_unstemmed	Internet of things-based soil sensing platform for Ganoderma boninense infection detection in oil palm seedlings using machine learning
title_sort	internet of things-based soil sensing platform for ganoderma boninense infection detection in oil palm seedlings using machine learning
granting_institution	Universiti Putra Malaysia
publishDate	2021
url	http://psasir.upm.edu.my/id/eprint/104208/1/MOHD%20HAMIM%20BIN%20ABDUL%20AZIZ%20-%20IR.pdf
_version_	1776100418322956288

Internet of things-based soil sensing platform for Ganoderma boninense infection detection in oil palm seedlings using machine learning

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