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Seed Health – VideometerLab detects seed-borne fungi

Seed Health – VideometerLab detects seed-borne fungi

Analysis of seeds and their health

One of the Videometer’s applications is seed. Spectral imaging allows for the analysis of seed purity, germination and vigor, the health of seeds, and seed treatment. In this article, we will focus on seed health and how to automate it with Videometer’s technology.

Seed health is crucial for crop production, as their diseases or defects can affect further production. Moreover, if it not detected fast enough, the quality of seeds can have a detrimental effect, for instance, in propagation.

Seed quality assessment is determined by various characteristics such as:

  • Varietal and analytical purity
  • Germination capacity
  • Vigour
  • Size
  • Uniformity
  • Variety identification and classification
  • Detection of insect damage
  • Fungi infection
  • Prediction of chemical composition
  • Surface structure
  • Seed color
  • Morphology

Multispectral imaging application in seed health

Multispectral vision and AI can automate time-consuming and laborious manual work. With the VideometerLab, there is no need for washing tests, embryo count methods, or the use of chemicals. This instrument and its technology allow for no sample preparation. Furthermore, they provides fast and accurate analysis without destructing the sample.

Detect seed-borne fungi with accuracy and speed – Seed Health

Using Videometer’s technology it is possible to detect seed-borne fungi more precisely in a shorter time span. The following scientific paper explores how Multispectral Imaging (MSI) combined with machine vision has been used as an alternative method to detect Drechslera Avenae Sharif in black oat seeds. Learn more about the study and how to automate your seed health assessment here. Below you can see an image showing the Videometer software depicting fungi in RGB, Grayscale, and Jet for nCDA transformation purposes.

Seed Health analysis in black oats using spectral imaging - Videometer
Seed Analysis using Hyperspectral Imaging – Videometer Applications

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

Learn more about Videometer’s capacity within seed area

 

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