Classification of fruits using Probabilistic Neural Networks - Improvement using color features

Nur Badariah Ahmad Mustafa, Kumutha Arumugam, Syed Khaleel Ahmed, Zainul Abidin Md Sharrif

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

10 Citations (Scopus)

Abstract

This paper presents a novel approach for the development of an intelligent fruit sorting system using techniques from Digital Image Processing and Artificial Neural Networks. The aim is to develop a fast and effective classification method along with a target of 100% efficiency. Five fruits; i.e., apples, bananas, carrots, mangoes and oranges were analysed and seventeen features were extracted based on the fruits' morphological and colour characteristics. A regular digital camera was used to acquire the images, and all manipulations were performed in a MATLAB/SIMULINK environment. The results obtained were a significant improvement over a previous study.

Original languageEnglish
Title of host publicationTENCON 2011 - 2011 IEEE Region 10 Conference
Subtitle of host publicationTrends and Development in Converging Technology Towards 2020
Pages264-269
Number of pages6
DOIs
Publication statusPublished - 2011
Event2011 IEEE Region 10 Conference: Trends and Development in Converging Technology Towards 2020, TENCON 2011 - Bali, Indonesia
Duration: 21 Nov 201124 Nov 2011

Other

Other2011 IEEE Region 10 Conference: Trends and Development in Converging Technology Towards 2020, TENCON 2011
CountryIndonesia
CityBali
Period21/11/1124/11/11

Fingerprint

Fruits
Color
Neural networks
Digital cameras
Sorting
MATLAB
Image processing

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Ahmad Mustafa, N. B., Arumugam, K., Khaleel Ahmed, S., & Sharrif, Z. A. M. (2011). Classification of fruits using Probabilistic Neural Networks - Improvement using color features. In TENCON 2011 - 2011 IEEE Region 10 Conference: Trends and Development in Converging Technology Towards 2020 (pp. 264-269). [6129105] https://doi.org/10.1109/TENCON.2011.6129105
Ahmad Mustafa, Nur Badariah ; Arumugam, Kumutha ; Khaleel Ahmed, Syed ; Sharrif, Zainul Abidin Md. / Classification of fruits using Probabilistic Neural Networks - Improvement using color features. TENCON 2011 - 2011 IEEE Region 10 Conference: Trends and Development in Converging Technology Towards 2020. 2011. pp. 264-269
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Ahmad Mustafa, NB, Arumugam, K, Khaleel Ahmed, S & Sharrif, ZAM 2011, Classification of fruits using Probabilistic Neural Networks - Improvement using color features. in TENCON 2011 - 2011 IEEE Region 10 Conference: Trends and Development in Converging Technology Towards 2020., 6129105, pp. 264-269, 2011 IEEE Region 10 Conference: Trends and Development in Converging Technology Towards 2020, TENCON 2011, Bali, Indonesia, 21/11/11. https://doi.org/10.1109/TENCON.2011.6129105

Classification of fruits using Probabilistic Neural Networks - Improvement using color features. / Ahmad Mustafa, Nur Badariah; Arumugam, Kumutha; Khaleel Ahmed, Syed; Sharrif, Zainul Abidin Md.

TENCON 2011 - 2011 IEEE Region 10 Conference: Trends and Development in Converging Technology Towards 2020. 2011. p. 264-269 6129105.

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

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T1 - Classification of fruits using Probabilistic Neural Networks - Improvement using color features

AU - Ahmad Mustafa, Nur Badariah

AU - Arumugam, Kumutha

AU - Khaleel Ahmed, Syed

AU - Sharrif, Zainul Abidin Md

PY - 2011

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N2 - This paper presents a novel approach for the development of an intelligent fruit sorting system using techniques from Digital Image Processing and Artificial Neural Networks. The aim is to develop a fast and effective classification method along with a target of 100% efficiency. Five fruits; i.e., apples, bananas, carrots, mangoes and oranges were analysed and seventeen features were extracted based on the fruits' morphological and colour characteristics. A regular digital camera was used to acquire the images, and all manipulations were performed in a MATLAB/SIMULINK environment. The results obtained were a significant improvement over a previous study.

AB - This paper presents a novel approach for the development of an intelligent fruit sorting system using techniques from Digital Image Processing and Artificial Neural Networks. The aim is to develop a fast and effective classification method along with a target of 100% efficiency. Five fruits; i.e., apples, bananas, carrots, mangoes and oranges were analysed and seventeen features were extracted based on the fruits' morphological and colour characteristics. A regular digital camera was used to acquire the images, and all manipulations were performed in a MATLAB/SIMULINK environment. The results obtained were a significant improvement over a previous study.

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Ahmad Mustafa NB, Arumugam K, Khaleel Ahmed S, Sharrif ZAM. Classification of fruits using Probabilistic Neural Networks - Improvement using color features. In TENCON 2011 - 2011 IEEE Region 10 Conference: Trends and Development in Converging Technology Towards 2020. 2011. p. 264-269. 6129105 https://doi.org/10.1109/TENCON.2011.6129105