Application of statistical and neural network model for oil palm yield study

This thesis presents an exploratory study on modelling of oil palm (OP) yield using statistical and artificial neural network approach. Even though Malaysia is one of the largest producers of palm oil, research on modelling of OP yield is still at its infancy. This study began by exploring the commo...

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Main Author:	Khamis, Azme
Format:	Thesis
Language:	English
Published:	2005
Subjects:	SB Plant culture
Online Access:	http://eprints.utm.my/id/eprint/1280/1/AzmeKhamisPFS2005.pdf
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id	my-utm-ep.1280
record_format	uketd_dc
spelling	my-utm-ep.12802018-02-20T03:43:32Z Application of statistical and neural network model for oil palm yield study 2005-12 Khamis, Azme SB Plant culture This thesis presents an exploratory study on modelling of oil palm (OP) yield using statistical and artificial neural network approach. Even though Malaysia is one of the largest producers of palm oil, research on modelling of OP yield is still at its infancy. This study began by exploring the commonly used statistical models for plant growth such as nonlinear growth model, multiple linear regression models and robust M regression model. Data used were OP yield growth data, foliar composition data and fertiliser treatments data, collected from seven stations in the inland and coastal areas provided by Malaysian Palm Oil Board (MPOB). Twelve nonlinear growth models were used. Initial study shows that logistic growth model gave the best fit for modelling OP yield. This study then explores the causality relationship between OP yield and foliar composition and the effect of nutrient balance ratio to OP yield. In improving the model, this study explores the use of neural network. The architecture of the neural network such as the combination activation functions, the learning rate, the number of hidden nodes, the momentum terms, the number of runs and outliers data on the neural networkâ€™s performance were also studied. Comparative studies between various models were carried out. The response surface analysis was used to determine the optimum combination of fertiliser in order to maximise OP yield. Saddle points occurred in the analysis and ridge analysis technique was used to overcome the saddle point problem with several alternative combinations fertiliser levels considered. Finally, profit analysis was performed to select and identify the fertiliser combination that may generate maximum yield 2005-12 Thesis http://eprints.utm.my/id/eprint/1280/ http://eprints.utm.my/id/eprint/1280/1/AzmeKhamisPFS2005.pdf application/pdf en public phd doctoral Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	SB Plant culture
spellingShingle	SB Plant culture Khamis, Azme Application of statistical and neural network model for oil palm yield study
description	This thesis presents an exploratory study on modelling of oil palm (OP) yield using statistical and artificial neural network approach. Even though Malaysia is one of the largest producers of palm oil, research on modelling of OP yield is still at its infancy. This study began by exploring the commonly used statistical models for plant growth such as nonlinear growth model, multiple linear regression models and robust M regression model. Data used were OP yield growth data, foliar composition data and fertiliser treatments data, collected from seven stations in the inland and coastal areas provided by Malaysian Palm Oil Board (MPOB). Twelve nonlinear growth models were used. Initial study shows that logistic growth model gave the best fit for modelling OP yield. This study then explores the causality relationship between OP yield and foliar composition and the effect of nutrient balance ratio to OP yield. In improving the model, this study explores the use of neural network. The architecture of the neural network such as the combination activation functions, the learning rate, the number of hidden nodes, the momentum terms, the number of runs and outliers data on the neural networkâ€™s performance were also studied. Comparative studies between various models were carried out. The response surface analysis was used to determine the optimum combination of fertiliser in order to maximise OP yield. Saddle points occurred in the analysis and ridge analysis technique was used to overcome the saddle point problem with several alternative combinations fertiliser levels considered. Finally, profit analysis was performed to select and identify the fertiliser combination that may generate maximum yield
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Khamis, Azme
author_facet	Khamis, Azme
author_sort	Khamis, Azme
title	Application of statistical and neural network model for oil palm yield study
title_short	Application of statistical and neural network model for oil palm yield study
title_full	Application of statistical and neural network model for oil palm yield study
title_fullStr	Application of statistical and neural network model for oil palm yield study
title_full_unstemmed	Application of statistical and neural network model for oil palm yield study
title_sort	application of statistical and neural network model for oil palm yield study
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2005
url	http://eprints.utm.my/id/eprint/1280/1/AzmeKhamisPFS2005.pdf
_version_	1747814372504240128

Application of statistical and neural network model for oil palm yield study

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