Pomegranate Fruit Quality Assessment Using Machine Intelligence and Wavelet Features

Open access


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.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • APEDA 2015. Pomegranate. In: Study on identification of export oriented integrated infrastructure for agri products from Maharashtra & Gujarat. Agriculture Produce Export Development Authority pp. 20–22.

  • Arefi A. Motlagh A.M. Mollazade K. Teimourlou R.F. 2011. Recognition and localization of ripen tomato based on machine vision. Australian Journal of Crop Science 5(10): 1144–1149.

  • Babu K.D. Marathe R.A. Jadhav V.T. 2012. Post harvest management of pomegranate. ICAR – National Research Centre on Pomegranate Solapur India 116 p.

  • Benagi V.I. Nargund V. Balikai R. Ravikumar M. 2009. Pomegranate – Identification and Management of Diseases Insect Pests and Disorders. University of Agricultural Sciences Dharwad India.

  • Clement J. Novas N. Gazquez J.A. Manzano-Agugliaro F. 2013. An active contour computer algorithm for the classification of cucumbers. Computers and Electronics in Agriculture 92: 75–81. DOI: 10.1016/j.compag.2013.01.006.

  • Deepa P. Geethalakshmi S.N. 2011. Improved water-shed segmentation for apple fruit grading. Proceedings of the International Conference on Process Automation Control and Computing IEEE 5 p. DOI: 10.1109/pacc.2011.5979003.

  • Dua S. Acharya U.R. Chowriappa P. Sree S.V. 2012. Wavelet-based energy features for glaucomatous image classification. IEEE Transactions on Information Technology in Biomedicine 16(1): 80–87. DOI: 10.1109/titb.2011.2176540.

  • Font D. Tresanchez M. Pallejà T. Teixidó M. Martinez D. Moreno J. Palacín J. 2014. An image processing method for in-line nectarine variety verification based on the comparison of skin feature histogram vectors. Computers and Electronics in Agriculture 102: 112–119. DOI: 10.1016/j.com-pag.2014.01.013.

  • Ghazali K.H. Mansor M.F. Mustafa M.M. Hussain A. 2007. Feature extraction technique using discrete wavelet transform for image classification. Proceedings of the 5th Student Conference on Research and Development IEEE 4 p. DOI: 10.1109/scored.2007.4451366.

  • Gonzalez R.C. Woods R.E. Eddins S.L. 2009. Digital Image Processing Using MATLAB 2nd edition. Gatesmark Publishing 827 p.

  • Hazra T.K. Guhathakurta R. 2016. Comparing wavelet and wavelet packet image denoising using thresholding techniques. International Journal of Science and Research 5(6): 790–796. DOI: 10.21275/v5i6.nov164305.

  • Jamil N. Mohamed A. Abdullah S. 2009. Automated grading of palm oil fresh fruit bunches (FFB) using neuro-fuzzy technique. Proceedings of the International Conference of Soft Computing and Pattern Recognition IEEE pp. 245–249. DOI: 10.1109/socpar.2009.57.

  • Mustafa N.B.A. Ahmed S.K. Ali Z. Yit W.B. Abidin A.A.Z. Sharrif Z.A.M. 2009. Agricultural produce sorting and grading using support vector machines and fuzzy logic. Proceedings of the International Conference on Signal and Image Processing Applications IEEE pp. 391–396. DOI: 10.1109/ic-sipa.2009.5478684.

  • Omid M. Abbasgolipour M. Keyhani A. Mohtasebi S.S. 2010. Implementation of an efficient image processing algorithm for grading raisins. International Journal of Signal and Image Processing 1(1): 31–34.

  • Rocha A. Hauagge D.C. Wainer J. Goldenstein S. 2010. Automatic fruit and vegetable classification from images. Computers and Electronics in Agriculture 70(1): 96–104. DOI: 10.1016/j.compag.2009.09.002.

  • Teimouri N. Omid M. Mollazade K. Rajabipour A. 2014. A novel artificial neural networks assisted segmentation algorithm for discriminating almond nut and shell from background and shadow. Computers and electronics in agriculture 105: 34–43. DOI: 10.1016/j.compag.2014.04.008.

  • Youwen T. Tianlai L. Yan N. 2008. The recognition of cucumber disease based on image processing and support vector machine. Proceedings of the Congress on Image and Signal Processing 2: 262–267. DOI: 10.1109/cisp.2008.29.

Journal information
Impact Factor

CiteScore 2018: 0.51

SCImago Journal Rank (SJR) 2018: 0.207
Source Normalized Impact per Paper (SNIP) 2018: 0.497

Cited By
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 439 285 17
PDF Downloads 406 281 17