Classification for the ripeness of papayas using artificial neural network (ANN) and threshold rule

H. Saad, Aini Hussain

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

The main objective of this project is developing the technique to classify the ripeness of papaya into 3 categories, which is immature, mature and over mature systematically based on their mean RGB value components. This system involved the process of collecting samples with different level of ripeness, image processing and image classification by using artificial neural network and threshold rule. Collecting papaya sample is done by using digital camera with 3.2 mega pixels. Image processing stage involves procedure such as edge detection, morphological operation and masking operation. 18 samples were used as training for artificial neural network. In order to see whether the both method mention above can classify the image correctly, another 32 images was used as testing. From the result obtained, it was shown that the artificial neural network can generally classify the ripeness of papaya better than threshold rule. This is because it can classify up to 30 samples correctly while threshold rule only 27 samples. Developing a program totally by using Matlab version 7.0 can help classification process successfully.

Original languageEnglish
Title of host publicationSCOReD 2006 - Proceedings of 2006 4th Student Conference on Research and Development "Towards Enhancing Research Excellence in the Region"
Pages132-136
Number of pages5
DOIs
Publication statusPublished - 2006
Event2006 4th Student Conference on Research and Development "Towards Enhancing Research Excellence in the Region", SCOReD 2006 - Shah Alam
Duration: 27 Jun 200628 Jun 2006

Other

Other2006 4th Student Conference on Research and Development "Towards Enhancing Research Excellence in the Region", SCOReD 2006
CityShah Alam
Period27/6/0628/6/06

Fingerprint

Neural networks
Image processing
Image classification
Digital cameras
Edge detection
Pixels
Testing

Keywords

  • Artificial neural network
  • Edge detection
  • Morphological operation
  • Threshold rule

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Saad, H., & Hussain, A. (2006). Classification for the ripeness of papayas using artificial neural network (ANN) and threshold rule. In SCOReD 2006 - Proceedings of 2006 4th Student Conference on Research and Development "Towards Enhancing Research Excellence in the Region" (pp. 132-136). [4339325] https://doi.org/10.1109/SCORED.2006.4339325

Classification for the ripeness of papayas using artificial neural network (ANN) and threshold rule. / Saad, H.; Hussain, Aini.

SCOReD 2006 - Proceedings of 2006 4th Student Conference on Research and Development "Towards Enhancing Research Excellence in the Region". 2006. p. 132-136 4339325.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Saad, H & Hussain, A 2006, Classification for the ripeness of papayas using artificial neural network (ANN) and threshold rule. in SCOReD 2006 - Proceedings of 2006 4th Student Conference on Research and Development "Towards Enhancing Research Excellence in the Region"., 4339325, pp. 132-136, 2006 4th Student Conference on Research and Development "Towards Enhancing Research Excellence in the Region", SCOReD 2006, Shah Alam, 27/6/06. https://doi.org/10.1109/SCORED.2006.4339325
Saad H, Hussain A. Classification for the ripeness of papayas using artificial neural network (ANN) and threshold rule. In SCOReD 2006 - Proceedings of 2006 4th Student Conference on Research and Development "Towards Enhancing Research Excellence in the Region". 2006. p. 132-136. 4339325 https://doi.org/10.1109/SCORED.2006.4339325
Saad, H. ; Hussain, Aini. / Classification for the ripeness of papayas using artificial neural network (ANN) and threshold rule. SCOReD 2006 - Proceedings of 2006 4th Student Conference on Research and Development "Towards Enhancing Research Excellence in the Region". 2006. pp. 132-136
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