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Comparative study on the effect of activation functions in neural network for rubber tree diseases detection

The rubber tree (Hevea brasiliensis) is grown extensively in South-East Asia, especially in Malaysia, for the production of natural rubber and, increasingly, timber. However, rubber tree generally attacked by pests or diseases such as root disease, white root, red root, brown root, leaf disease and...

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Main Author: Mohd. Rasidi, Haryani
Format: Thesis
Language:English
Published: 2011
Subjects:
Q Science (General)
Online Access:http://eprints.utm.my/id/eprint/32825/5/HaryaniMohdRasidiMFSKSM2011.pdf
http://eprints.utm.my/id/eprint/32825/
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spelling my.utm.328252018-05-27T07:55:14Z http://eprints.utm.my/id/eprint/32825/ Comparative study on the effect of activation functions in neural network for rubber tree diseases detection Mohd. Rasidi, Haryani Q Science (General) The rubber tree (Hevea brasiliensis) is grown extensively in South-East Asia, especially in Malaysia, for the production of natural rubber and, increasingly, timber. However, rubber tree generally attacked by pests or diseases such as root disease, white root, red root, brown root, leaf disease and the others. In this study, four types of diseases will be seen namely Fusicoccum, Corynespora, Collelotrichum and Oidium. Generally known that neural networks may be used for nonlinear analysis of complex data. As such, the purpose of this study is to evaluate the usefulness of Artificial Neural Networks (ANNs) applied to rubber tree for diseases detection. The ANNs used in the present study were based on a feed forward layered model with input, hidden, and output layers, on which a backpropagation learning model was implemented. The focus of study also to investigate the effect of activation functions on accuracy, efficiency and performance of disease detection. Four activation functions will be compare which are Hyperbolic Tangent Sigmoid, Linear, Radial Basis and Triangular. This study also will be focus on how to convert an image data to the conventional input data. The preprocessing process of the sample image such as image enhancement, image filtering has been performed before the process feature extraction will be applied. The techniques that will be used for feature extraction of image using the gabor filter method. The output of this filter will be used for the classification purpose in order to determine the characteristics of rubber tree diseases image. 10-fold cross validation techniques will be applied in order to measure the percentage of accurately of this classifier. 2011-05 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/32825/5/HaryaniMohdRasidiMFSKSM2011.pdf Mohd. Rasidi, Haryani (2011) Comparative study on the effect of activation functions in neural network for rubber tree diseases detection. Masters thesis, Universiti Teknologi Malaysia, Faculty of Computer Science and Information System.
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic Q Science (General)
spellingShingle Q Science (General)
Mohd. Rasidi, Haryani
Comparative study on the effect of activation functions in neural network for rubber tree diseases detection
description The rubber tree (Hevea brasiliensis) is grown extensively in South-East Asia, especially in Malaysia, for the production of natural rubber and, increasingly, timber. However, rubber tree generally attacked by pests or diseases such as root disease, white root, red root, brown root, leaf disease and the others. In this study, four types of diseases will be seen namely Fusicoccum, Corynespora, Collelotrichum and Oidium. Generally known that neural networks may be used for nonlinear analysis of complex data. As such, the purpose of this study is to evaluate the usefulness of Artificial Neural Networks (ANNs) applied to rubber tree for diseases detection. The ANNs used in the present study were based on a feed forward layered model with input, hidden, and output layers, on which a backpropagation learning model was implemented. The focus of study also to investigate the effect of activation functions on accuracy, efficiency and performance of disease detection. Four activation functions will be compare which are Hyperbolic Tangent Sigmoid, Linear, Radial Basis and Triangular. This study also will be focus on how to convert an image data to the conventional input data. The preprocessing process of the sample image such as image enhancement, image filtering has been performed before the process feature extraction will be applied. The techniques that will be used for feature extraction of image using the gabor filter method. The output of this filter will be used for the classification purpose in order to determine the characteristics of rubber tree diseases image. 10-fold cross validation techniques will be applied in order to measure the percentage of accurately of this classifier.
format Thesis
author Mohd. Rasidi, Haryani
author_facet Mohd. Rasidi, Haryani
author_sort Mohd. Rasidi, Haryani
title Comparative study on the effect of activation functions in neural network for rubber tree diseases detection
title_short Comparative study on the effect of activation functions in neural network for rubber tree diseases detection
title_full Comparative study on the effect of activation functions in neural network for rubber tree diseases detection
title_fullStr Comparative study on the effect of activation functions in neural network for rubber tree diseases detection
title_full_unstemmed Comparative study on the effect of activation functions in neural network for rubber tree diseases detection
title_sort comparative study on the effect of activation functions in neural network for rubber tree diseases detection
publishDate 2011
url http://eprints.utm.my/id/eprint/32825/5/HaryaniMohdRasidiMFSKSM2011.pdf
http://eprints.utm.my/id/eprint/32825/
_version_ 1643649152047906816
score 13.211869

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