Classification of Parus strawberry fruit by combining image processing techniques and intelligent methods

مجله پژوهشهای علوم و صنایع غذایی ایران

Article 1, Volume 16, Issue 6 - Serial Number 66, January and February 2021, Pages 87-99   

Document Type: Research Article-en

DOI: 10.22067/ifstrj.v17i3.84187

Authors

Farhad Fatehi¹; Hadi Samimi Akhijahani^{* 2}

¹Department of Mechanics of Biosystem Engineering, University of Tabriz

²Department of Biosystem Engineering, University of Kurdistan.

Abstract

Nowadays, in modern agriculture, the combination of image processing techniques and intelligent methods has been used to replace smart machine instead of humans. In this study, an artificial image processing and artificial neural network (ANN) method was used to classify strawberry fruit of Parus variety. In the first step, the fruit was divided into 6 classes (ANN outputs) by the expert, and 100 samples were randomly collected from each class. In the next step, the images of the samples were captured and three geometric properties with twelve color properties (as ANN inputs) were extracted. Optimum artificial neural network structures considering root mean squared error (RMSE) and correlation coefficient (R2) were investigated to classification process of the strawberry samples. Finally, the perceptron neural network with a structure of 6-18-15 was selected with an average accuracy of 83.83%.

Keywords

Artificial neural network; Image processing; Parus variety; Strawberry classification

References

Abdullah, M. Z., Mohamad-Saleh, J. Fathinul-Syahir, A. S., & Mohd-Azemi, B. M. N. (2006). Discrimination and classification of fresh-cut starfruits (Averrhoa carambola L.) using automated machine vision system. Journal of Food Engineering, 76(4), 506–523.

Castelo-Quispe, S., Banda-Tapia, J. D. Lopez-Paredes, M. N. Barrios-Aranibar, D., & Patino-Escarcina, R. (2013). Optimization of Brazil-Nuts Classification Process through Automation using Colour Spaces in Computer Vision. International Journal of Computer Information Systems and Industrial Management Applications. 5, 623-630.

Chen, Y. R., Chao, K., & Kim, M. S. (2002). Machine Vision Technology for Agricultural Applications. Computers and Electronics in Agriculture, 36 (2), 173-191.

Chia, K.S., Abdul Rahim, H., Abdul Rahim, R. 2012. Prediction of soluble solids content of pineapple via non-invasive low cost visible and shortwave near infrared spectroscopy and artificial neural network. Biosystems Engineering, 113, 158–165.

Darrow, G. M., M. S. Wilcox, W. D. Scott, and M. C. Hutchins. 1947. Breeding Strawberries for Vitamin C. Journal of Heredity, 38: 363-365.

Doving, A. andF. Mago. 2002. Methods of testing strawberry fruit firmness. Journal of Acta Agriculture Scandinavica, 52: 43-51.

FAO. 2017. FAOSTAT Agricultural Statistics Database. Retrieved from http://www.fao.org.

Feng, G., Qixin, C., & Masateru, N. (2008). Fruit detachment and classification method for strawberry harvesting robot. International Journal of Advanced Robotic Systems, 5(1), 4.

Leemans, V., & Destain, M. F. (2004). A real-time grading method of apples based on features extracted from defects. Journal of Food Engineering, 61, 83-89.

Liming, X., &Yanchao, Z. (2010). Automated strawberry grading system based on image processing. Computers and Electronics in Agriculture. 71, 32-39.

Mitcham, B. 1996. Quality assurance for Strawberries: A Case Study. Perishables Handling Newsletter Issue No. 85. Pages 6-9.

Mollazade, K., Omid, M., & Arefi, A. (2012). Comparing data mining classifiers for grading raisins based on visual features. Computers and Electronics in Agriculture, 84, 124-131.

Nagata, M., Bato, P. M., Mitarai, M., Qixin, C., & Kitahara, T. (2000). Study on sorting system for strawberry using machine vision (Part 1). Journal of the Japanese Society of Agricultural Machinery, 62(1), 100-110.

Nemzer, B., L. Vargas, X. Xia, and H. Feng. 2018. Phytochemical and physical properties of blueberries, tart cherries, strawberries, and cranberries as affected by different drying methods. Food Chemistry, 262: 242-250.

Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9, 62-66.

Riquelme, M. T., Barreiro, P. Ruiz-Altisent, M., & Valero, C. (2008). Olive classification according to external damage using image analysi. Journal of Food Engineering, 87, 371-379.

Salami, P., Ahmadi, H., Keyhani, A., & Sarsaifee, M. (2010). Strawberry post-harvest energy losses in Iran. Researcher, 2, 67-73.

Samimi Akhijahani, H. and J. Khodaei. 2011. Some physical properties of strawberry (Kurdistan varity). World Applied Sciences Journal, 13 (2): 256-212.

Schmidhuber, J. 2015. Deep learning in neural networks: An overview. Neural networks, 61, 85-117.

Singh S.P., Bansal, S., Ahuja, M., Parnami, S. 2015. Classification of apples using neural networks. International Journal of Science, Technology and Management, 4 (1); 1599-1605.

Taghavi, T. (2005). Strawberry production guide: sana publications, Tehran, Iran. (persian)

Torkashvand, A.M., Ahmadi, A., Layegh Nikravesh, N. 2017. Prediction of kiwifruit frmness using fruit mineral nutrient concentration by artificial neural network (ANN) and multiple linear regressions (MLR). Journal of Integrative Agriculture, 16(7): 1634–1644

Voca, S., Dobricevic, N., Dragovic-Uzelac, V., DuralijaDruzic, j., Cmelik, Z., & Babjelic, M.S. (2008). Fruit quality of new early ripening strawberry cultivars in Crotia. Journal of Food Technology and Biotechnology. 46, 292-298.