Detection ofDrechslera avenae(Eidam) Sharif [Helminthosporium avenae(Eidam)] in Black Oat Seeds (Avena strigosaSchreb) Using Multispectral Imaging

Author(s)

Franca-Silva, Fabiano ^[1] ; Queiroz Rego, Carlos Henrique ^[1] ; Guilhien Gomes-Junior, Francisco ^[1] ; Duarte de Moraes, Maria Heloisa ^[2] ; de Medeiros, Andre Dantas ^[3] ; da Silva, Clissia Barboza ^[4]

Affiliation

^[1] Univ Sao Paulo Luiz de Queiroz, Dept Crop Sci, Coll Agr, 11 Padua Dias Ave, BR-13418900 Piracicaba - Brazil

^[2] Univ Sao Paulo Luiz de Queiroz, Dept Plant Pathol & Nematol, Coll Agr, 11 Padua Dias Ave, BR-13418900 Piracicaba - Brazil

^[3] Univ Fed Vicosa, Dept Agron, Peter Henry Rolfs Ave, BR-36570900 Vicosa, MG - Brazil

^[4] Univ Sao Paulo, Lab Radiobiol & Environm, Ctr Nucl Energy Agr, 303 Centenario Ave, BR-13416000 Piracicaba, SP - Brazil

• Document type: Journal article
• Source: SENSORS; v. 20, n. 12 JUN 2020.
• Web of Science Citations: 7

Abstract

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 detectDrechslera avenae(Eidam) Sharif {[}Helminthosporium avenae(Eidam)] in black oat seeds (Avena strigosaSchreb). The seeds were inoculated withDrechslera 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 detectingD. avenaein 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. (AU)

• FAPESP's process: 17/15220-7 - Non-destructive image analysis methods for seed quality evaluation
• Grantee: Clíssia Barboza Mastrangelo
• Support Opportunities: Research Grants - Young Investigators Grants
• FAPESP's process: 18/03802-4 - Multi-user equipment approved in grant 2017/15220-7: imaging system VideoMeterLab
• Grantee: Clíssia Barboza Mastrangelo
• Support Opportunities: Multi-user Equipment Program