Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Pomegranate Fruit Quality Assessment Using Machine Intelligence and Wavelet Features

Pomegranate Fruit Quality Assessment Using Machine Intelligence and Wavelet Features AbstractQuality 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Horticultural Research de Gruyter

Pomegranate Fruit Quality Assessment Using Machine Intelligence and Wavelet Features

Download PDF
8 pages

Loading next page...
 
/lp/de-gruyter/pomegranate-fruit-quality-assessment-using-machine-intelligence-and-j6xPeg4xZ2

References (17)

Publisher
de Gruyter
Copyright
© 2018 Arun Kumar R. et al., published by Sciendo
eISSN
2300-5009
DOI
10.2478/johr-2018-0006
Publisher site
See Article on Publisher Site

Abstract

AbstractQuality 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.

Journal

Journal of Horticultural Research – de Gruyter

Published: Jun 1, 2018

There are no references for this article.