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

Rodrigo Cupertino Bernardes, André De Medeiros, Laercio da Silva, Leo Cantoni, Gustavo Ferreira Martins, Thiago Mastrangelo, Arthur Novikov and Clíssia Barboza Mastrangelo ()
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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

Agriculture, 2022, vol. 12, issue 11, 1-14

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.

Keywords: seed quality; convolutional neural networks; Triticum aestivum; Fusarium graminearum; RGB images; TBIO Toruk cultivar (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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