https://doi.org/10.4081/jae.2021.1160 Image analysis of real-time classification of cherry fruit from colour features
Published:23 December 2021
Abstract Views: 1326
PDF: 618
HTML: 26
HTML: 26
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
An image analysis algorithm for the classification of cherries in real time by processing their digitalized colour images was developed, and tested. A set of five digitalized images of colour pattern, corresponding to five colour classes defined for commercial cherries, was characterized. The algorithm performs the segmentation of the cheery image by rejecting the pixels of the background and keeping the image features corresponding to the coloured area of the fruit. A histogram analysis was carried out for the RGB and HSV colour spaces, where the Red and Hue components showed differences between each of the specified colour patterns of the exporting reference system. This information led to the development of a hybrid Bayesian classification algorithm based on the components R and H. Its accuracy was tested with a set of cherry samples within the colour range of interest. The algorithm was implemented by means of a real time C++ code in Microsoft Visual Studio environment. When testing, the algorithm showed a 100% effectiveness in classifying a sample set of cherries into the five standardized cherry classes. The components of the hardware-software system for implementing the methodology are low cost, thus ensuring an affordable commercial deployment.
Abdelghafour F., Rosu R., Keresztes B., Germain C., Da Costa J.P. 2019. A Bayesian framework for joint structure and colour based pixel-wise classification of grapevine proximal images. Comput. Electron. Agric. 158:345-57. DOI: https://doi.org/10.1016/j.compag.2019.02.017
Ahmad I.S., Reid J.F.1996. Evaluation of colour representations for maize images. J. Agric. Eng. Res. 63:185-96. DOI: https://doi.org/10.1006/jaer.1996.0020
Antonelli A., Cocchi M., Fava P., Foca G., Franchini G.C., Manzini D., Ulrici A. 2004. Automated evaluation of food colour by means of multivariate image analysis coupled to a wavelet-based classification algorithm. Analyt. Chim. Acta 515:3-13. DOI: https://doi.org/10.1016/j.aca.2004.01.005
Arivazhagan S., Shebiah R.N., Nidhyanandhan S.S., Ganesan L. 2010. Fruit recognition using colour and texture features. Inf. Sci. 1:90-4.
Blasco J., Aleixos N., Moltó E. 2003. Machine vision system for automatic quality grading of fruit. Biosyst. Engine. 85:415-23. DOI: https://doi.org/10.1016/S1537-5110(03)00088-6
Correa C., Valero C., Barreiro P., Diago M.P., Tardaguila J. 2012. Feature extraction on vineyard by Gustafson Kessel FCM and K-means. pp 481-484 in Electrotechnical Conference (MELECON), 16th IEEE Mediterranean, 25-28 March 2012. DOI: https://doi.org/10.1109/MELCON.2012.6196477
Ebner M. 2004. A parallel algorithm for colour constancy. J. Parallel Distrib. Comput. 64:79-88. DOI: https://doi.org/10.1016/j.jpdc.2003.06.004
Funck J.W., Zhong Y., Butler D.A., Brunner C.C., Forrer J.B. 2003. Image segmentation algorithms applied to wood defect detection. Comput. Electron. Agric. 41:157-79. DOI: https://doi.org/10.1016/S0168-1699(03)00049-8
Gan H., Lee W.S., Alchanatis V., Ehsani R., Schueller J.K. 2018. Immature green citrus fruit detection using colour and thermal images. Comput. Electron. Agric. 152:117-25. DOI: https://doi.org/10.1016/j.compag.2018.07.011
González R.C., Woods R.E. 2006. Tratamiento digital de imágenes. Addison-Wesley Longman, USA, pp. 800.
Kang H., Chen C. 2020. Fruit detection, segmentation and 3D visualisation of environments in apple orchards. Comput. Electron. Agric. 171:105302. DOI: https://doi.org/10.1016/j.compag.2020.105302
Khurram H., Chai D., Rassau A. 2018. A comprehensive review of fruit and vegetable classification techniques. Image Vision Comput. 80:24-44. DOI: https://doi.org/10.1016/j.imavis.2018.09.016
Kondo N. 2010. Automation on fruit and vegetable grading system and food traceability. Trends Food Sci. Technol. 21:145-52. DOI: https://doi.org/10.1016/j.tifs.2009.09.002
Kotsiantis S.B. 2007. Supervised machine learning: a review of classification techniques. Informatica 31:249-68.
Lee D.J., Schoenberger R., Archibald J., McCollum S. 2008. Development of a machine vision system for automatic date grading using digital reflective near-infrared imaging. J. Food Engine. 86:388-98. DOI: https://doi.org/10.1016/j.jfoodeng.2007.10.021
Leemans V., Magein H., Destain M.F. 2002. On-line fruit grading according to their external quality using machine vision. Biosyst. Engine. 83:397-404. DOI: https://doi.org/10.1006/bioe.2002.0131
Momeny M., Jahanbakhshi A., Jafarnezhad K., Zhang Y.D. 2020. Accurate classification of cherry fruit using deep CNN based on hybrid pooling approach. Postharvest Biol. Technol. 166:111204. DOI: https://doi.org/10.1016/j.postharvbio.2020.111204
Moreda G.P., Ortiz-Cañavate J., García-Ramos F.J., Ruiz-Altisent M. 2009. Non-destructive technologies for fruit and vegetable size determination - A review. J. Food Engine. 92:119-36. DOI: https://doi.org/10.1016/j.jfoodeng.2008.11.004
Paulsson N., Stocklassa B. 1999. A real-time colour image processing system for forensic fiber investigations. Foren. Sci. Int. 103:37-59. DOI: https://doi.org/10.1016/S0379-0738(99)00046-8
Paulus I., De Busscher R., Schrevens E. 1997. Use of image analysis to investigate human quality classification of apples. J. Agric. Eng. Res. 68:341-53. DOI: https://doi.org/10.1006/jaer.1997.0210
Rahimizadeh H., Marhaban M.H., Kamil R.M., Ismail N.B. 2009. Colour image segmentation based on bayesian theorem and kernel density estimation. Eur. J. Sci. Res. 26:430-6.
Rungpichayapicheta P., Mahayotheeb B., Naglea M., Khuwijitjarub P., Müllera J. 2016. Robust NIRS models for non-destructive prediction of postharvest fruit ripeness and quality in mango, Postharv. Biol. Technol. 11:31-40. DOI: https://doi.org/10.1016/j.postharvbio.2015.07.006
Singh N., Delwiche M.J., Scott R. 1993. Image analysis methods for real-time colour grading of stone fruit. Comput. Electron. Agric. 9:71-84. DOI: https://doi.org/10.1016/0168-1699(93)90030-5
Sowmya B., Sheelarani B. 2009. Colour image segmentation using soft computing techniques. Int. J. Soft Comput. Appl. 4:69-80.
Tan K., Suk Lee W., Gan H., Wang Sh. 2018. Recognizing blueberry fruit of different maturity using histogram-oriented gradients and colour features in outdoor scenes. Biosyst. Engine. 176:59-72. DOI: https://doi.org/10.1016/j.biosystemseng.2018.08.011
Wu G., Zhu Q., Huang M., Guo Y., Qin J. 2019. Automatic recognition of juicy peaches on trees based on 3D contour features and colour data. Biosyst. Engine. 188:1-13. DOI: https://doi.org/10.1016/j.biosystemseng.2019.10.002
Zhang Ch., Guo Ch., Liu F., Kong W., He Y., Lou B. 2016. Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector machine. J. Food Engine. 179:11-8. DOI: https://doi.org/10.1016/j.jfoodeng.2016.01.002
How to Cite
Reyes, J. F. . (2021) “Image analysis of real-time classification of cherry fruit from colour features”, Journal of Agricultural Engineering, 52(4). doi: 10.4081/jae.2021.1160.
Copyright (c) 2021 the Author(s) 
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.