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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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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