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Detection of Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in Black Oat Seeds (Avena strigosa Schreb) Using Multispectral Imaging

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Department of Crop Science, University of São Paulo-Luiz de Queiroz College of Agriculture, 11 Pádua Dias Avenue, 13418-900 Piracicaba, Brazil
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Department of Plant Pathology and Nematology, University of São Paulo-Luiz de Queiroz College of Agriculture, 11 Pádua Dias Avenue, Piracicaba 13418-900, Brazil
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Department of Agronomy, Universidade Federal de Viçosa, Peter Henry Rolfs Avenue, Viçosa MG 36570-900, Brazil
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Laboratory of Radiobiology and Environment, University of São Paulo-Center for Nuclear Energy in Agriculture, 303 Centenário Avenue, Piracicaba SP 13416-000, Brazil
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(12), 3343; https://doi.org/10.3390/s20123343
Received: 8 May 2020 / Revised: 4 June 2020 / Accepted: 8 June 2020 / Published: 12 June 2020
(This article belongs to the Section Optical Sensors)
Conventional methods for detecting seed-borne fungi are laborious and time-consuming, requiring specialized analysts for characterization of pathogenic fungi on seed. Multispectral imaging (MSI) combined with machine vision was used as an alternative method to detect Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in black oat seeds (Avena strigosa Schreb). The seeds were inoculated with Drechslera avenae (D. avenae) and then incubated for 24, 72 and 120 h. Multispectral images of non-infested and infested seeds were acquired at 19 wavelengths within the spectral range of 365 to 970 nm. A classification model based on linear discriminant analysis (LDA) was created using reflectance, color, and texture features of the seed images. The model developed showed high performance of MSI in detecting D. avenae in black oat seeds, particularly using color and texture features from seeds incubated for 120 h, with an accuracy of 0.86 in independent validation. The high precision of the classifier showed that the method using images captured in the Ultraviolet A region (365 nm) could be easily used to classify black oat seeds according to their health status, and results can be achieved more rapidly and effectively compared to conventional methods. View Full-Text
Keywords: machine vision; Pyrenophora avenae; reflectance; seed quality; seed pathology machine vision; Pyrenophora avenae; reflectance; seed quality; seed pathology
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MDPI and ACS Style

França-Silva, F.; Rego, C.H.Q.; Gomes-Junior, F.G.; Moraes, M.H.D.d.; Medeiros, A.D.d.; Silva, C.B.d. Detection of Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in Black Oat Seeds (Avena strigosa Schreb) Using Multispectral Imaging. Sensors 2020, 20, 3343. https://doi.org/10.3390/s20123343

AMA Style

França-Silva F, Rego CHQ, Gomes-Junior FG, Moraes MHDd, Medeiros ADd, Silva CBd. Detection of Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in Black Oat Seeds (Avena strigosa Schreb) Using Multispectral Imaging. Sensors. 2020; 20(12):3343. https://doi.org/10.3390/s20123343

Chicago/Turabian Style

França-Silva, Fabiano, Carlos H.Q. Rego, Francisco G. Gomes-Junior, Maria H.D.d. Moraes, André D.d. Medeiros, and Clíssia B.d. Silva 2020. "Detection of Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in Black Oat Seeds (Avena strigosa Schreb) Using Multispectral Imaging" Sensors 20, no. 12: 3343. https://doi.org/10.3390/s20123343

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