Automatic characterisation of dye decolourisation in fungal strains using expert, traditional, and deep features

Abstract

Fungi have diverse biotechnological applications in, among others, agriculture, bioenergy generation, or remediation of polluted soil and water. In this context, culture media based on colour change in response to degradation of dyes are particularly relevant, but measuring dye decolourisation of fungal strains mainly relies on a visual and semiquantitative classification of colour intensity changes. Such a classification is a subjective, time-consuming, and difficult to reproduce process. In order to deal with these problems, we have performed a systematic evaluation of different image-classification approaches considering ad hoc expert features, traditional computer vision features, and transfer-learning features obtained from deep neural networks. Our results favour the transfer learning approach reaching an accuracy of 96.5% in the evaluated dataset. In this paper, we provide the first, at least up to the best of our knowledge, method to automatically characterise dye decolourisation level of fungal strains from images of inoculated plates.

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Acknowledgements

This work was partially supported by the Ministerio de Economía y Competitividad [MTM2014-54151-P, MTM2017-88804-P], and Agencia de Desarrollo Económico de La Rioja [2017-I-IDD-00018].

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Correspondence to Jónathan Heras or Gerardo Vázquez-Marrufo.

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Arredondo-Santoyo, M., Domínguez, C., Heras, J. et al. Automatic characterisation of dye decolourisation in fungal strains using expert, traditional, and deep features. Soft Comput 23, 12799–12812 (2019). https://doi.org/10.1007/s00500-019-03832-8

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Keywords

  • Fungal decolourisation
  • Image classification
  • Computer vision
  • Deep learning
  • Transfer learning