Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (6/6 displayed)

  • 2022Dynamic fracture behaviour of additively manufactured composite materialscitations
  • 2022Mechanical characterisation of AM nylon-matrix carbon-fibre-reinforced composite in tension13citations
  • 2022Dynamic fracture of additively manufactured continuous-fibre composites under ballistic impact: experimental and numerical study1citations
  • 2021Image processing algorithm to estimate ice-plant leaf area from RGB images under different light conditions10citations
  • 2021Dynamic fracture behaviour of additively manufactured polymers and composites under ballistic impact7citations
  • 2019Influence of Ni doping in a lead-halide and a lead-free halide perovskites for optoelectronic applications88citations

Places of action

Chart of shared publication
Silberschmidt, Vadim V.
4 / 524 shared
Baxevanakis, K. P.
4 / 20 shared
Reza, M. N.
1 / 1 shared
Ali, M.
1 / 47 shared
Islam, S.
1 / 2 shared
Chung, S. O.
1 / 1 shared
Kiraga, S.
1 / 1 shared
Chowdhury, M.
1 / 2 shared
Hadi, Abdul
1 / 1 shared
Podder, J.
1 / 2 shared
Chart of publication period
2022
2021
2019

Co-Authors (by relevance)

  • Silberschmidt, Vadim V.
  • Baxevanakis, K. P.
  • Reza, M. N.
  • Ali, M.
  • Islam, S.
  • Chung, S. O.
  • Kiraga, S.
  • Chowdhury, M.
  • Hadi, Abdul
  • Podder, J.
OrganizationsLocationPeople

article

Image processing algorithm to estimate ice-plant leaf area from RGB images under different light conditions

  • Reza, M. N.
  • Ali, M.
  • Islam, S.
  • Islam, M. N.
  • Chung, S. O.
  • Kiraga, S.
  • Chowdhury, M.
Abstract

<jats:title>Abstract</jats:title><jats:p>The productivity of horticultural crops in an artificial light condition are highly influenced by the structure of plant and the area coverage. Accurate measurement of leaf area is very important for predicting plant water demand and optimal growth. In this paper, we proposed an image processing algorithm to estimate the ice-plant leaf area from the RGB images under the artificial light condition. The images were taken using a digital camera and the RGB images were transformed to grayscale images. A binary masking was applied from a grayscale image by classifying each pixel, belonging to the region of interest from the background. Then the masked images were segmented and the leaf region was filled using region filling technique. Finally, the leaf area was calculated from the number of pixel and using known object area. The experiment was carried out in three different light conditions with same plant variety (Ice-plant, <jats:italic>Mesembryanthemum crystallinum)</jats:italic>. The results showed that the correlation between the actual and measured leaf area was found over 0.97 (R<jats:sup>2</jats:sup>:0.973) by our proposed method. Different light condition also showed significant impact on plant growth. Our results inspired further research and development of algorithms for the specific applications.</jats:p>

Topics
  • impedance spectroscopy
  • experiment