Phenotypic evaluation of seedling vigour-related traits in a set of rice lines

Padmashree, R; Magar, Nakul D; Barbadikar, Kalyani M.; Phule, Amol S.; Senguttuvel, P; Maganti, Sheshu Madhav; Siddaiah, Anantha M; Balakrishnan, Divya; Chanappa, Gireesh; Manasa, V; Lokesha, R; Honnappa, -

doi:10.58297/pacy8180

Cited by 1 publication

(1 citation statement)

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“…AFM is feasible to probe the number of graphene layers but it cannot detect the number of layers quickly [19], taking several up to a few hours. Even though AFM and Raman spectroscopy provide accurate characterization, they are time-intensive and may require sample preparation [20]. For alleviating this hindrance, image processing techniques have been utilized.…”

Section: Introductionmentioning

confidence: 99%

Versatile recognition of graphene layers from optical images under controlled illumination through green channel correlation method

Sahriar,

Abed,

Nirjhar

et al. 2023

Nanotechnology

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In this study, a simple yet versatile method is proposed for identifying the number of exfoliated graphene layers transferred on an oxide substrate from optical images, utilizing a limited number of input images for training, paired with a more traditional number of a few thousand well-published Github images for testing and predicting. The method specifically leverages the red, green, and blue (RGB) color channels of image pixels and creates a correlation between the green channel of the background and the green channel of the various layers of graphene. This method proves to be a feasible alternative to deep learning-based graphene recognition and traditional microscopic analysis. The proposed methodology performs well under conditions where the effect of surrounding light on the graphene-on-oxide sample is minimum and allows rapid identification of the various graphene layers. The study additionally addresses the functionality of the proposed methodology with nonhomogeneous lighting conditions, showcasing successful prediction of graphene layers from images that are lower in quality compared to typically published in literature. In all, the proposed methodology opens up the possibility for the non-destructive identification of graphene layers from optical images by utilizing a new and versatile method that is quick, inexpensive, and works well with fewer images that are not necessarily of high quality.

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Section: Introductionmentioning

confidence: 99%