Pomegranate Fruit Quality Assessment Using Machine Intelligence and Wavelet Features

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.2013.01.006.10.1016/j.compag.2013.01.006Abierto DOISearch in Google Scholar

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.2011.5979003.10.1109/pacc.2011.5979003Abierto DOISearch in Google Scholar

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.2011.2176540.10.1109/titb.2011.217654022113813Abierto DOISearch in Google Scholar

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.2014.01.013.10.1016/j.com-pag.2014.01.013Abierto DOISearch in Google Scholar

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 5^th Student Conference on Research and Development, IEEE, 4 p. DOI: 10.1109/scored.2007.4451366.10.1109/scored.2007.4451366Abierto DOISearch in Google Scholar

Gonzalez R.C., Woods R.E., Eddins S.L. 2009. Digital Image Processing Using MATLAB, 2^nd edition. Gatesmark Publishing, 827 p.Search in Google Scholar

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.nov164305.10.21275/v5i6.NOV164305Abierto DOISearch in Google Scholar

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.2009.57.10.1109/socpar.2009.57Abierto DOISearch in Google Scholar

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.2009.5478684.10.1109/ic-sipa.2009.5478684Abierto DOISearch in Google Scholar

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.Search in Google Scholar

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.2009.09.002.10.1016/j.compag.2009.09.002Abierto DOISearch in Google Scholar

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.2014.04.008.10.1016/j.compag.2014.04.008Abierto DOISearch in Google Scholar

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.2008.29.10.1109/CISP.2008.29Abierto DOISearch in Google Scholar

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