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Case Studies2019-05-16T12:06:24+00:00

Case Studies

Shanghai Jiao Tong University tested responses to root zone water availability

Researchers of the Shanghai Jiao Tong University used a LemnaTec Greenhouse Scanalyzer to assess the impact of root zone water availability on shoot growth performance of pakchoi (Brassica rapa chinensis) plants. Aim was to establish a method to discriminate root zone water availability levels by applying shoot phenotyping methods. Visible light [...]

Automated growth stage determination with Rothamsted Field Scanalyzer

Current phenotyping technology fulfills tasks that are congruent with many “classical” measurements in agronomy or botany. Howerver, despite measuring the same object, data might lack comparability. One challenge is matching growth stages with non-invasive phenotyping data. Scientists at Rothamsted Research used machine learning methods to derive growth stage information in wheat [...]

Water stress trials at Arkansas State University

Using an HTS Scanalyzer with RGB-, NIR-, and fluorescence cameras, researchers generated 4320 images of Arabidopsis plants in a drought study. Growth responses, water content, and chlorophyll-originating fluorescence signals were used to physiologically characterise the responses of the plants towards water limitation in different severities. Together with ionomic data, phenotypic data [...]

Deep field phenotyping becomes reality at Rothamsted Research

In partnership with LemnaTec, the global plant phenotyping specialist, scientists at Rothamsted Research have implemented a Field Scanalyzer capable of continuously monitoring the development of crops under field conditions. The facility will be used initially to understand the development of numerous pre-breeding wheat lines that have been generated through the Wheat Genetic Improvement Network [...]

Phenomics at the Arkansas Center for Plant Powered Production

Manual phenotyping of large sets of plants requires a great deal of resources and expertise and is typically not feasible for detection of subtle phenotypes. Therefore, there is a growing need to develop quantitative, reproducible, and highly automated phenotyping systems to analyze large numbers of plants. [...]