Abstract
The quality of fruit plays a fundamental role in their marketing and is mainly defined by its shape, color, and size. The classification process is traditionally done manually and takes time. The use of image processing techniques can help this task. Some methodologies for image classification are presented, using deep neural networks. A set of combinations between Convolution Neural Networks (CNN), deep neural networks (DNN) using Gabor filter, over RGB and grayscale images, extracting texture properties of a GLCM (Gray Level Co-occurrence Matrices) is used in this project. Background segmentation, contrast enhancement, and data augmentation are also used to improve generalization and minimize overfitting. Applying it to a set of tropical fruits resulted in an excellent set of results, above 95% on average.
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References
Arunachalam, S., Kshatriya, H.H., Meena, M.: Identification of defects in fruits using digital image processing. Int. J. Comput. Sci. Eng. 6, 637–640 (2018). https://doi.org/10.26438/ijcse/v6i10.637640
Capizzi, G., Sciuto, G., Napoli, C., Tramontana, E., Woźniak, M.: A novel neural networks-based texture image processing algorithm for orange defects classification. Int. J. Comput. Sci. Appl. 13, 45–60 (2016)
Devi, P., Vijayarekha, K.: Machine vision applications to locate fruits, detect defects and remove noise: a review. Rasayan J. Chem. 7, 104–113 (2014)
Dubey, S.R., Jalal, A.S.: Adapted approach for fruit disease identification using images. Int. J. Comput. Vis. Image Processing (IJCVIP) 2, 44–58 (2012). https://doi.org/10.4018/ijcvip.2012070104
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC 3(6), 610–621 (1973). https://doi.org/10.1109/TSMC.1973.4309314
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988). https://doi.org/10.1007/BF00133570
Moradi, G., Shamsi, M., Sedaghi, M.H., Alsharif, M.R.: Fruit defect detection from color images using ACM and MFCM algorithms. In: 2011 International Conference on Electronic Devices, Systems and Applications (ICEDSA), pp. 182–186 (2011). https://doi.org/10.1109/ICEDSA.2011.5959033
Pandey, R., Naik, S., Marfatia, R.: Image processing and machine learning for automated fruit grading system: a technical review. Int. J. Comput. Appl. 81, 29–39 (2013). https://doi.org/10.5120/14209-2455
Sahu, D., Potdar, R.M.: Defect identification and maturity detection of mango fruits using image analysis. Am. J. Artif. Intell. 1, 5–14 (2017)
Xul, Q., Zou, X., Zhao, J.: On-line detection of defects on fruit by machinevision systems based on three-color-cameras systems. In: Zhao, C., Li, D. (eds.) Computer and Computing Technologies in Agriculture II, vol. 3, pp. 2231–2238. Springer, US, Boston, MA (2009)
Yogesh, Dubey, A.K.: Fruit defect detection based on speeded up robust feature technique. In: 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), pp. 590–594 (2016). https://doi.org/10.1109/ICRITO.2016.7785023
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This study was financed in part by the Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq, and Fundação de Apoio à Pesquisa do Distrito Federal - FAPDF
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Aguiar, H.T., Vasconcelos, R.C.S. (2022). Identification of External Defects on Fruits Using Deep Learning. In: Pinho, A.J., Georgieva, P., Teixeira, L.F., Sánchez, J.A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2022. Lecture Notes in Computer Science, vol 13256. Springer, Cham. https://doi.org/10.1007/978-3-031-04881-4_45
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