Potato defects classification and localization with convolutional neural networks

S. Marino; A. Smolarz; P. Beauseroy

doi:10.1117/12.2521264

16 July 2019 Potato defects classification and localization with convolutional neural networks

S. Marino, A. Smolarz, P. Beauseroy

Proceedings Volume 11172, Fourteenth International Conference on Quality Control by Artificial Vision; 111720G (2019) https://doi.org/10.1117/12.2521264
Event: Fourteenth International Conference on Quality Control by Artificial Vision, 2019, Mulhouse, France

Abstract

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.

Citation Download Citation

S. Marino, A. Smolarz, and P. Beauseroy "Potato defects classification and localization with convolutional neural networks", Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 111720G (16 July 2019); https://doi.org/10.1117/12.2521264

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