Ms. Sofia Marino Profile

Sofia Marino

at Univ de Technologie Troyes

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Proceedings Article | 16 July 2019 Paper

Potato defects classification and localization with convolutional neural networks

S. Marino, A. Smolarz, P. Beauseroy

Proceedings Volume 11172, 111720G (2019) https://doi.org/10.1117/12.2521264

KEYWORDS: Convolutional neural networks, Image segmentation, Network architectures, Databases, Image classification, Statistical analysis, Convolution, MATLAB, Control systems, Machine vision

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Various defects can appear on the surface of a potato, producing an adverse effect on their market price. For several years, manual methods have been applied to classify this tuber, which caused certain drawbacks such as: high-cost, high-processing time and subjective results. In this paper we introduce a deep-learning based method to classify and localize defects in potatoes with the aim to automate the quality control task. An extensive dataset was created including six potato categories: healthy, damaged, greening, black dot, common scab and black scurf. Then, a convolutional neural network (CNN) was trained with this dataset in order to achieve the classification task. We also propose to leverage the localization capability of the trained network to localize the region of the classified defect. Finally, a global evaluation was done in a test set, where 4 different sides images were taken into account to represent one tuber. Experimental results with different CNN architectures are shown. We achieved an average F1-score of 0.94 for the classification task. The localization performance is measured qualitatively by a heat map output, which shows that the proposed method accurately localize the defects.

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