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Classification of wheat kernels infected with fungi of the genus Fusarium using discriminative classifiers and neural networks

Chilean journal of agricultural research, 2019-01, Vol.79 (1), p.48-55

COPYRIGHT 2019 Instituto de Investigaciones Agropecuarias ;2019. This work is published under https://creativecommons.org/licenses/by-sa/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. ;ISSN: 0718-5839 ;ISSN: 0718-5820 ;EISSN: 0718-5839 ;DOI: 10.4067/S0718-58392019000100048

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  • Title:
    Classification of wheat kernels infected with fungi of the genus Fusarium using discriminative classifiers and neural networks
  • Author: Ropelewska, Ewa
  • Subjects: Accuracy ; AGRICULTURE, MULTIDISCIPLINARY ; AGRONOMY ; Barley ; Basis functions ; Bayesian analysis ; Blight ; Cereals ; Chromatography ; Classification ; Classifiers ; Color ; Decision trees ; Discriminant analysis ; Discrimination ; Food ; Fungi ; Fusarium ; Fusarium head blight ; Grain ; Identification ; Kernels ; Metabolites ; Model accuracy ; Morphology ; Multilayer perceptrons ; Mycoses ; Neural networks ; Radial basis function ; Science ; Wheat
  • Is Part Of: Chilean journal of agricultural research, 2019-01, Vol.79 (1), p.48-55
  • Description: Fusarium head blight (FHB) compromises the processing suitability and nutritional value of grain, and it causes significant crop losses. The aim of the study was to develop models for the classification of wheat (Triticum aestivum L.) kernels infected with fungi and healthy wheat kernels. Wheat kernels were classified with the use of Decision Tree, Rule-based, Bayes, Lazy, Meta and Function classifiers, as well as multilayer perceptron (MLP), radial basis function (RBF) and probabilistic neural networks (PNN). Twenty textures were selected from RGB, Lab, XYZ colour spaces each, for every wheat variety and each kernel side. Accuracy ranged from 82% for the dorsal side of kernels for Naive Bayes and IBk classifiers to 100% for the ventral side of kernels for IBk, FLDA and Naive Bayes classifiers. Classification accuracy was highest in the model based on texture attributes from Lab colour space. The final model of 20 attributes from Lab colour space was applied to a set of kernels from all wheat varieties, analysed on the ventral side. The accuracy of the classification model ranged from 94% to 98%, depending on the applied classifier. The models developed with the use of neural networks were characterised by overall classification accuracy of above 99% for MLP networks, above 96% for RBF networks and above 97% for PNN. The developed models indicate that analyses should be performed on the ventral side of kernels based on textures from Lab colour space.
  • Publisher: Chillán: Instituto de Investigaciones Agropecuarias
  • Language: English;Portuguese
  • Identifier: ISSN: 0718-5839
    ISSN: 0718-5820
    EISSN: 0718-5839
    DOI: 10.4067/S0718-58392019000100048
  • Source: SciELO
    Alma/SFX Local Collection
    Bioline International
    ProQuest Central
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