Classification of fruits using Probabilistic Neural Networks - Improvement using color features
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., a...
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my.uniten.dspace-50132017-11-14T04:49:22Z Classification of fruits using Probabilistic Neural Networks - Improvement using color features Mustafa, N.B.A. Arumugam, K. Ahmed, S.K. Sharrif, Z.A.Md. 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. © 2011 IEEE. 2017-11-14T03:21:18Z 2017-11-14T03:21:18Z 2011 Conference Paper 10.1109/TENCON.2011.6129105 en IEEE Region 10 Annual International Conference, Proceedings/TENCON 2011, Article number 6129105, Pages 264-269 |
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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. © 2011 IEEE. |
| format |
Conference Paper |
| author |
Mustafa, N.B.A. Arumugam, K. Ahmed, S.K. Sharrif, Z.A.Md. |
| spellingShingle |
Mustafa, N.B.A. Arumugam, K. Ahmed, S.K. Sharrif, Z.A.Md. Classification of fruits using Probabilistic Neural Networks - Improvement using color features |
| author_facet |
Mustafa, N.B.A. Arumugam, K. Ahmed, S.K. Sharrif, Z.A.Md. |
| author_sort |
Mustafa, N.B.A. |
| title |
Classification of fruits using Probabilistic Neural Networks - Improvement using color features |
| title_short |
Classification of fruits using Probabilistic Neural Networks - Improvement using color features |
| title_full |
Classification of fruits using Probabilistic Neural Networks - Improvement using color features |
| title_fullStr |
Classification of fruits using Probabilistic Neural Networks - Improvement using color features |
| title_full_unstemmed |
Classification of fruits using Probabilistic Neural Networks - Improvement using color features |
| title_sort |
classification of fruits using probabilistic neural networks - improvement using color features |
| publishDate |
2017 |
| _version_ |
1644493590271885312 |
| score |
13.211869 |