Artificial Neural Networks Based Red Palm Weevil (Rynchophorus Ferrugineous, Olivier) Recognition System

Saleh Mufleh Al-Saqer; Ghulam Mubashar Hassan

doi:10.3844/ajabssp.2011.356.364

Article Details

Artificial Neural Networks Based Red Palm Weevil (Rynchophorus Ferrugineous, Olivier) Recognition System

Authors:

Saleh Mufleh Al-Saqer | Ghulam Mubashar Hassan

Publisher:

Science Publications

Journal:

American Journal of Agricultural and Biological Sciences Vol.6 Issue 3, pp.356-364

Document Type:

Article

Publication Date:

4/2011

Document Language:

English

Document ID (Org No.):

2001395847

pISSN:

1557-4997

DOI:

10.3844/ajabssp.2011.356.364

Keywords:

Abstract:

 Problem statement: The most dangerous insect for the existence of palm trees in entire world is Red Palm Weevil (scientifically named as Rynchophorus Ferrugineous, Oliveir). The proposed research is conducted to develop an identification system for Automated Wireless Red Palm Weevil Detection and exterminated. The core idea of the proposed research is to develop software that can utilize image processing and Artificial neural network techniques to identify Red Palm Weevil and distinguishes it from other insects found in palm trees habitat. Approach: Images are taken and processed with image processing techniques. Afterwards, Artificial neural network is used to recognize the presence of Red Palm Weevil in an image. Two different feed-forward supervised learning algorithms of Artificial neural network are used i.e., scaled conjugate gradient and Conjugate Gradient with Powell/Beale Restarts Algorithms. Different Artificial neural network sizes are tested using both algorithms and are compared to find an optimal algorithm and network. The training, verification and testing of the Artificial neural network is accomplished by using a database of 319 images of Red Palm Weevil and 93 images of other insects which are usually found around palm trees. Images are randomly selected from database for training, verification and testing with a fixed percentage of 80, 10 and 10 respectively. Training for every selected set of configuration is repeated 10 times. Results: The best results for scaled conjugate gradient Algorithm is obtained by three layers ANN consuming 221 sec and 167 Epochs while its average success in identification of Red Palm Weevil and other insect is 99 and 93% respectively. On the other hand, best performance of Conjugate Gradient with Powell/Beale Restarts Algorithm is observed by using three layers ANN which consumed 183 sec and 109 Epochs for training while its average success in identification of Red Palm Weevil and other insect is 99.5 and 93.5% respectively. Conclusion: It is gleaned out that 3-layers Artificial neural network using Conjugate Gradient with Powell/Beale Restarts Algorithm for feed-forward supervised learning is optimal for identification of Red Palm Weevil.

Article Details

Artificial Neural Networks Based Red Palm Weevil (Rynchophorus Ferrugineous, Olivier) Recognition System

Authors:

Saleh Mufleh Al-Saqer | Ghulam Mubashar Hassan

Publisher:

Science Publications

Journal:

American Journal of Agricultural and Biological Sciences Vol.6 Issue 3, pp.356-364

Document Type:

Article

Publication Date:

4/2011

Document Language:

English

Document ID (Org No.):

2001395847

pISSN:

1557-4997

DOI:

10.3844/ajabssp.2011.356.364

Red palm weevil

automated recognition system

artificial neural supervised learning

neural network

scaled conjugate gradient

conjugate gradient with powell/beale restarts

insect recognition

Issues

Article Details

Authors: Saleh Mufleh Al-Saqer | Ghulam Mubashar Hassan

Publisher: Science Publications

Journal: American Journal of Agricultural and Biological Sciences Vol.6 Issue 3, pp.356-364

Document Type: Article

Publication Date: 4/2011

Document Language: English

Document ID (Org No.): 2001395847

pISSN: 1557-4997

DOI: 10.3844/ajabssp.2011.356.364

Issues

Authors:

Saleh Mufleh Al-Saqer | Ghulam Mubashar Hassan

Publisher:

Science Publications

Journal:

American Journal of Agricultural and Biological Sciences Vol.6 Issue 3, pp.356-364

Document Type:

Article

Publication Date:

4/2011

Document Language:

English

Document ID (Org No.):

2001395847

pISSN:

1557-4997

DOI:

10.3844/ajabssp.2011.356.364