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

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 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.

Integrating Optical Imaging Tools for Rapid and Non-invasive Characterization of Seed Quality: Tomato (Solanum lycopersicum L.) and Carrot (Daucus carota L.) as Study Cases

“…Multispectral imaging is a non-destructive technology able to integrate the conventional vision and spectroscopy technique to obtain at same time spatial and spectral information of an object (Shrestha et al, 2015 ; Mastrangelo et al, 2019 ; França-Silva et al, 2020 ), with accurate measurements of uniform and non-homogeneous samples. Advanced multispectral imaging systems have the great advantage to assess simultaneously multiple components, providing information about texture, color, shape, size and chemical composition for quality assurance.…”

The Use of Multispectral Imaging and Single Seed and Bulk Near-Infrared Spectroscopy to Characterize Seed Covering Structures: Methods and Applications in Seed Testing and Research

“…Seeds located too close to each other may touch or even overlap and cause occlusion leading to poorer segmentation and adding noise to the extracted features. To prevent seeds from moving, when placing them in the Petri dish, they may be fixed with double-sided tape [29,32] or placed on an insert with small recesses inside the Petri dish. When placing the seeds, it is important to consider which side is most relevant for the application and thus should be facing the imaging sensor.…”

“…First demonstrated in spinach by detection of Stemphylium botryosum, Cladosporium spp., Fusarium spp., Verticillium spp. or Alternaria alternate [26], and recently by detection of Drechslera avenae and Helminthosporium avenae in black oat/oats seeds (Avena strigosa) [32], Fusarium pallidoroseum, Rhizoctonia solani, and Aspergillus sp. in cowpea [29].…”

Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality

“…Briefly, this technique is based on sequential exposure of an object to light at different wavelengths integrated with computer systems to recognize physicochemical parameters from biological samples. In the context of seed quality, spectral imaging is mainly based on texture [ 4 ], physical and chemical attributes associated with insect damages [ 5 ], fungal infections [ 6 ], among others, but with limitation to examine internal tissues. Meanwhile, X-ray imaging has proved great potential to collect data from internal structures (e.g.…”