<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Quality assessment is an important concern in the post-harvest marketing of fruits. Manual quality assessment of pomegranate fruits poses various problems because of human operators. In the present paper, an efficient machine vision system is designed and implemented in order to assess the quality of pomegranate fruits. The main objectives of the present study are (1) to adopt a best pre-processing module, (2) to select best class of features and (3) to develop an efficient machine learning technique for quality assessment of pomegranates. The sample images of pomegranate fruits are captured using a custom-made image acquisition system. Two sets of features, namely, spatial domain feature set and wavelet feature set are extracted for all of the sample images. Experiments are conducted by training both artificial neural networks (ANNs) and support vector machines (SVMs) using both sets of features. The results of the experiments illustrated that ANNs outperform SVMs with a difference in the accuracy of 12.65%. Further, the selection of wavelet featureset for training yielded more accurate results against spatial domain feature set.</p></abstract>

Quality assessment is an important concern in the post-harvest marketing of fruits. Manual quality assessment of pomegranate fruits poses various problems because of human operators. In the present paper, an efficient machine vision system is designed and implemented in order to assess the quality of pomegranate fruits. The main objectives of the present study are (1) to adopt a best pre-processing module, (2) to select best class of features and (3) to develop an efficient machine learning technique for quality assessment of pomegranates. The sample images of pomegranate fruits are captured using a custom-made image acquisition system. Two sets of features, namely, spatial domain feature set and wavelet feature set are extracted for all of the sample images. Experiments are conducted by training both artificial neural networks (ANNs) and support vector machines (SVMs) using both sets of features. The results of the experiments illustrated that ANNs outperform SVMs with a difference in the accuracy of 12.65%. Further, the selection of wavelet featureset for training yielded more accurate results against spatial domain feature set.

Pomegranate Fruit Quality Assessment Using Machine Intelligence and Wavelet Features

Journal of Horticultural Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Quality assessment is an important concern in the post-harvest marketing of fruits. Manual quality assessment of pomegranate fruits poses various problems...

Pomegranate Fruit Quality Assessment Using Machine Intelligence and Wavelet Features

Data publikacji: 29 cze 2018

Zakres stron: 53 - 60

Otrzymano: 01 mar 2018

Przyjęty: 01 cze 2018

DOI: https://doi.org/10.2478/johr-2018-0006

Słowa kluczowe
Pomegranate quality assessment, wavelet features for fruit quality analysis, machine intelligence in pomegranate quality assessment, ANN for fruit quality assessment

© 2018 Arun Kumar R. et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Pomegranate Fruit Quality Assessment Using Machine Intelligence and Wavelet Features

Data publikacji: 29 cze 2018

Zakres stron: 53 - 60

Otrzymano: 01 mar 2018

Przyjęty: 01 cze 2018

DOI: https://doi.org/10.2478/johr-2018-0006

Słowa kluczowePomegranate quality assessment, wavelet features for fruit quality analysis, machine intelligence in pomegranate quality assessment, ANN for fruit quality assessment

© 2018 Arun Kumar R. et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
Pomegranate quality assessment, wavelet features for fruit quality analysis, machine intelligence in pomegranate quality assessment, ANN for fruit quality assessment