2015-07-27   facebooktwitterrss

Phenotyping Technology Beats Sugar Beet Diseases

Global plant phenotyping specialist LemnaTec has established an automated process to identify disease in sugar beet leaves which it believes will significantly improve the yield of the crops worldwide.

Sugar beet leaves collected from a field trial at the Julius Kühn-Institut’s Institute for Plant Protection in Field Crops and Grassland were examined for signs of colour changes, which can indicate signs of disease. The Lemnatec HTS Scanalyzer was used to detect and characterize green, yellow and brown regions in leaf tissues based on colour classification determined by LemnaTec’s sophisticated phenotyping software. In this way, it was possible for the researchers to quantify colour changes on leaf surfaces, which typically denoted lesions.


The Scanalyzer

Marcus Jansen, Chief Scientist at Lemnatec, says:
“Diseases in plant production are a major threat for mankind's food supply, plant-derived products and ornamental plants. All parts of the plant may potentially be subjected to attack by pests or diseases and the methods a pathogen or pest uses to damage a plant are diverse, ranging from eating parts of the plant to microbial infection and destruction at tissue or cellular level.”

“With this latest research we want to ensure that the crop responsible for 20% of the world’s sugar supply has the highest possible yield for its farmers in the future.”

Using a series of morphological filters, LemnaTec was able to detect colour changes in sugar beet images caused by Uromyces betae, Cercospora beticola and Ramularia beticola. The mean relative amount of green, yellow and brown tissue per leaf reflected the visual scoring well. Moreover, the image analysis provided meaningful values of symptom counts per leaf.

Control leaf discs also showed spots, although on average only on 0.2% of the investigated leaf tissue. The size and shape of these spots suggest that they could be caused either by Uromyces or Cercospora; however, it is also probable that they had no pathogenic origin but were due to mechanical damage or random cell death events.

Marcus Jansen continues: “We believe that repeating this analysis with a larger dataset will enable us to establish a footprint to identify disease symptoms based on their morphological shape parameters (e.g. roundness, compactness). This process will also quantify and classify colour changes in leaves caused by various biotic and abiotic stresses.”


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