A Decade Of Expertise In Digital Phenotyping To Support Your Research Endeavors
Our image analysis pipeline ingests images from drones, ground based imaging systems, satellites and uses well-vetted algorithms and AI-powered deep learning modules to calculate an ever growing range of traits as we are always pushing the boundaries of plant phenotyping. Discover below our plant phenotyping portfolio of uses cases to dive deeper into Hiphen's crop assessment expertise. Also, as we have high R&D capabilities, we can develop and provide new traits upon request for your specific application.
Border Effect: Computed at plant emergence, this trait measures the possibility that a plot development...
This trait refers to the percentage of flowers present in the plot. It is expressed...
Flower cover area refers to the surface of flower pixels within the plot, expressed in...
The Normalized Difference Red-Edge Index is based on the red-edge band which is very sensitive...
This trait represents the average row length measured between the first and last plant detected...
QPAR represents the accumulated quantity of PAR (Photosynthetically Active Radiation) intercepted by the plants during...
This trait indicates the probability that a plot has been affected by lodging. Lodging is...
The MERIS Terrestrial Chlorophyll Index was designed to estimate chlorophyll content especially from MERIS datasets....
The Normalized Difference Vegetation Index is one of the most commonly used indices for monitoring...
This trait refers to the heterogeneity measured from the Green Cover calculation. The plot studied...
This trait refers to the heterogeneity measured from the organ count. The plot studied is...
This trait evaluates the chlorophyll content at leaf level. A high Cab value indicates that...