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Deep-Learning Approach for Fusarium Head Blight Detection in Wheat Seeds Using Low-Cost Imaging Technology

Author

Listed:
  • Rodrigo Cupertino Bernardes

    (Department of Entomology, Universidade Federal de Viçosa (UFV), Viçosa 36570-900, Minas Gerais, Brazil)

  • André De Medeiros

    (Department of Agronomy, Universidade Federal de Viçosa (UFV), Viçosa 36570-900, Minas Gerais, Brazil)

  • Laercio da Silva

    (Department of Agronomy, Universidade Federal de Viçosa (UFV), Viçosa 36570-900, Minas Gerais, Brazil)

  • Leo Cantoni

    (Department of Agronomy, Universidade Federal de Viçosa (UFV), Viçosa 36570-900, Minas Gerais, Brazil)

  • Gustavo Ferreira Martins

    (Department of General Biology, Universidade Federal de Viçosa (UFV), Viçosa 36570-900, Minas Gerais, Brazil)

  • Thiago Mastrangelo

    (Laboratory of Radiobiology and Environment, Center for Nuclear Energy in Agriculture (CENA/USP), Piracicaba 13416-000, São Paulo, Brazil)

  • Arthur Novikov

    (Timber Industry Faculty, Voronezh State University of Forestry and Technologies Named after G.F. Morozov, 394087 Voronezh, Russia)

  • Clíssia Barboza Mastrangelo

    (Laboratory of Radiobiology and Environment, Center for Nuclear Energy in Agriculture (CENA/USP), Piracicaba 13416-000, São Paulo, Brazil)

Abstract

Modern techniques that enable high-precision and rapid identification/elimination of wheat seeds infected by Fusarium head blight (FHB) can help to prevent human and animal health risks while improving agricultural sustainability. Robust pattern-recognition methods, such as deep learning, can achieve higher precision in detecting infected seeds using more accessible solutions, such as ordinary RGB cameras. This study used different deep-learning approaches based on RGB images, combining hyperparameter optimization, and fine-tuning strategies with different pretrained convolutional neural networks (convnets) to discriminate wheat seeds of the TBIO Toruk cultivar infected by FHB. The models achieved an accuracy of 97% using a low-complexity design architecture with hyperparameter optimization and 99% accuracy in detecting FHB in seeds. These findings suggest the potential of low-cost imaging technology and deep-learning models for the accurate classification of wheat seeds infected by FHB. However, FHB symptoms are genotype-dependent, and therefore the accuracy of the detection method may vary depending on phenotypic variations among wheat cultivars.

Suggested Citation

  • Rodrigo Cupertino Bernardes & André De Medeiros & Laercio da Silva & Leo Cantoni & Gustavo Ferreira Martins & Thiago Mastrangelo & Arthur Novikov & Clíssia Barboza Mastrangelo, 2022. "Deep-Learning Approach for Fusarium Head Blight Detection in Wheat Seeds Using Low-Cost Imaging Technology," Agriculture, MDPI, vol. 12(11), pages 1-14, October.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:11:p:1801-:d:957392
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    References listed on IDEAS

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    1. Khalied Albarrak & Yonis Gulzar & Yasir Hamid & Abid Mehmood & Arjumand Bano Soomro, 2022. "A Deep Learning-Based Model for Date Fruit Classification," Sustainability, MDPI, vol. 14(10), pages 1-16, May.
    2. Karl Gruber, 2017. "Agrobiodiversity: The living library," Nature, Nature, vol. 544(7651), pages 8-10, April.
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