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

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 fu...

Full description

Saved in:
Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2019-12, Vol.23 (23), p.12799-12812
Main Authors: Arredondo-Santoyo, Marina, Domínguez, César, Heras, Jónathan, Mata, Eloy, Pascual, Vico, Vázquez-Garcidueñas, Mª Soledad, Vázquez-Marrufo, Gerardo
Format: Article
Language:English
Subjects:
Artificial Intelligence
Artificial neural networks
Classification
Color
Computational Intelligence
Computer vision
Control
Decoloring
Dyes
Engineering
Enzymes
Evaluation
Fungi
Image classification
Laboratories
Machine learning
Mathematical Logic and Foundations
Mechatronics
Metabolites
Methodologies and Application
Robotics
Soil pollution
Soil remediation
Soil water
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-019-03832-8