Neural network model for oil palm yield modelling

This research presents a study on the development of a model for oil palm yield using neural network approach. The structure of this neural network requires the identification of the input variables and the output. We identified that the percentages of nitrogen, phosphorus, potassium, calcium and ma...

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Main Authors: Khamis, Azme, Ismail, Zuhaimy, Haron, Khalid, Mohammed, Ahmad Tarmizi
Format: Article
Published: 2006
Subjects:
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author Khamis, Azme
Ismail, Zuhaimy
Haron, Khalid
Mohammed, Ahmad Tarmizi
author_facet Khamis, Azme
Ismail, Zuhaimy
Haron, Khalid
Mohammed, Ahmad Tarmizi
author_sort Khamis, Azme
collection ePrints
description This research presents a study on the development of a model for oil palm yield using neural network approach. The structure of this neural network requires the identification of the input variables and the output. We identified that the percentages of nitrogen, phosphorus, potassium, calcium and magnesium in leave were used as input variables and fresh fruit bunch was used as the target variable. An investigation of the combinations of activation function in the input layer to the hidden layer and the hidden layer to the output layer found that each combination also affects the neural network performance. The effect of the learning rate, momentum term, number of runs and number of hidden nodes was also investigated. The number of hidden nodes was found to significantly affect the neural network performance. However, the learning rate, momentum term and number of runs were found to have an insignificant effect on the neural network performance. Using R2 values the suitability of the models were measured. Results demonstrate that the neural network model out performed regression analysis, which can be considered as alternative in modeling of oil palm yield
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spelling utm.eprints-90492010-04-06T04:57:40Z http://eprints.utm.my/9049/ Neural network model for oil palm yield modelling Khamis, Azme Ismail, Zuhaimy Haron, Khalid Mohammed, Ahmad Tarmizi QA Mathematics This research presents a study on the development of a model for oil palm yield using neural network approach. The structure of this neural network requires the identification of the input variables and the output. We identified that the percentages of nitrogen, phosphorus, potassium, calcium and magnesium in leave were used as input variables and fresh fruit bunch was used as the target variable. An investigation of the combinations of activation function in the input layer to the hidden layer and the hidden layer to the output layer found that each combination also affects the neural network performance. The effect of the learning rate, momentum term, number of runs and number of hidden nodes was also investigated. The number of hidden nodes was found to significantly affect the neural network performance. However, the learning rate, momentum term and number of runs were found to have an insignificant effect on the neural network performance. Using R2 values the suitability of the models were measured. Results demonstrate that the neural network model out performed regression analysis, which can be considered as alternative in modeling of oil palm yield 2006 Article PeerReviewed Khamis, Azme and Ismail, Zuhaimy and Haron, Khalid and Mohammed, Ahmad Tarmizi (2006) Neural network model for oil palm yield modelling. Journal of Applied Science, 6 (2). pp. 391-399. http://adsabs.harvard.edu/abs/2006JApSc...6..391K
spellingShingle QA Mathematics
Khamis, Azme
Ismail, Zuhaimy
Haron, Khalid
Mohammed, Ahmad Tarmizi
Neural network model for oil palm yield modelling
title Neural network model for oil palm yield modelling
title_full Neural network model for oil palm yield modelling
title_fullStr Neural network model for oil palm yield modelling
title_full_unstemmed Neural network model for oil palm yield modelling
title_short Neural network model for oil palm yield modelling
title_sort neural network model for oil palm yield modelling
topic QA Mathematics
work_keys_str_mv AT khamisazme neuralnetworkmodelforoilpalmyieldmodelling
AT ismailzuhaimy neuralnetworkmodelforoilpalmyieldmodelling
AT haronkhalid neuralnetworkmodelforoilpalmyieldmodelling
AT mohammedahmadtarmizi neuralnetworkmodelforoilpalmyieldmodelling