Abstract
This paper presents an approach to classify the fruit maturity of fruit grading system when a higher level of accuracy and easy interpretability of the estimate model is desired. The proposed method automatically generates membership functions (MFs) and constructs the associated fuzzy rules (FRs). The proposed approach is applied to a case study of Harumanis mango fruit grading system. The task is to classify the fruit maturity and grade using agronomic image data set acquired by digital camera. The parameters of the MFs are adjusted by the learning algorithm for the training data. This MF is then used to generate the MFbox using hyperbox-type fuzzy partition of feature space to generate FRs from training instance to deal with the features data. FRs are extracted from the flexible MFs and MFbox. As a case study, the proposed method is applied to Harumanis data set with 108 instances (input–output pairs), two real-valued inputs, and one output. Analysis results show that the proposed maturity classifier yields an accuracy of 98 %. The developed maturity classifier can act as an instrument in determining the correct mango fruit maturity category.
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References
Leemans, V., Destain, M.F., Magein, H.: Quality fruit grading by colour machine vision: Defect recognition. Acta Hortic. 517, 405–412 (2000)
Lorestani, A.N., Omid, M., Bagheri-Shooraki, S., Borghei, A.M., Tabatabaeefar, A.: Design and evaluation of a fuzzy logic based decision support system for grading of golden delicious apples. Int. J. Agric. Biol. 8(4), 440–444 (2006)
Liming, X., Yanchao, Z.: Automated strawberry grading system based on image processing. Comput. Electron. Agric. 71, S32–S39 (2010)
Shah Rizam, M.S.B., Farah Yasmin, A.R., Ahmad Ihsan, M.Y., Shazana, K.: Non-destructive watermelon ripeness determination using image processing and artificial neural network (ANN). World Acad. Sci. Eng. Technol. 50, 538–542 (2009)
Razak, M.A., Mahmod, O., Mohd Nazari, A.B., Khairul Adilah, A., Rosli, R.T.: Fuzzy ripening mango index using RGB colour sensor model. J. Arts Sci. Commer. 5(2), 1–9 (2014)
Slaughter, D.C.: Nondestructive Maturity Assessment Methods for Mango: A Review of Literature and Identification of Future Research Needs, pp. 1–18. National Mango Board, Orlando (2009)
Zadeh, L.A., Fellow, L.: Fuzzy logic = computing with words. IEEE Trans. Fuzzy Syst. 4(2), 103–111 (1996)
Birle, S., Hussein, M.A., Becker, T.: Fuzzy logic control and soft sensing applications in food and beverage processes. Food Control 29(1), 254–269 (2013)
Kader, A.: Mango quality attributes and grade standards: a review of available information and identification of future research needs. Technical report to the National Mango Board Davis, CA, USA. Kader Consulting Services (2008)
Moreda, G.P., Muñoz, M.A., Ruiz-Altisent, M., Perdigones, A.: Shape determination of horticultural produce using two-dimensional computer vision—a review. J. Food Eng. 108(2), 245–261 (2012)
Hong, T., Lee, C.: Induction of fuzzy rules and membership functions from training examples. Fuzzy Sets Syst. 84(95), 33–47 (1996)
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Ahmad, K.A.b., Othman, M., Mansor, A.R., Abu Bakar, M.N. (2017). A Fuzzy Learning Algorithm for Harumanis Maturity Classification. In: Ahmad, AR., Kor, L., Ahmad, I., Idrus, Z. (eds) Proceedings of the International Conference on Computing, Mathematics and Statistics (iCMS 2015). Springer, Singapore. https://doi.org/10.1007/978-981-10-2772-7_1
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DOI: https://doi.org/10.1007/978-981-10-2772-7_1
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