About ReGENius
ReGENius is an expert system for forest ecosystem and wildlife habitat analyses.
The AI was trained on my lectures and teaching materials for students of forestry and agricultural sciences as well as trainees in the profession of Revierjäger/Revierjägerin(professional gamekeeper). It is aimed at everyone active in the fields of forestry, hunting, wildlife management, conservation, and habitat management who need to make well-founded decisions—whether selecting suitable plant species for reforestation under climate change or assessing a habitat’s suitability for wildlife.
The idea for this AI arose because, in recent years, my students and I repeatedly encountered plant species during our forest and wildlife-ecological habitat assessments that could not be identified with conventional field guides for Central Europe. As these species appeared, forest communities and wildlife habitats also changed. Unlike a textbook, ReGENius offers a fast, easily updatable way to address the relevant topics.
Core Competencies of ReGENius
Forest type and site classification (ReGENius)
Identification of potential natural vegetation (PNV), site units, and forest communities based on your site data. A key function of ReGENius is evaluating vegetation with regard to its forage value. Discrepancies between grasses and herbaceous plants and the target tree species can exacerbate the forest–game conflict. ReGENius can develop and present options for action here.
Currently, ReGENius achieves >95% correct assignment of potential natural vegetation (PNV). Not familiar with plant identification? No problem. Simply take a few good photos with your smartphone and upload them to the user interface. From that, the AI can already generate an initial result. Just place the plant to be considered on a tissue or similar and take a sharp, well-lit photo.
Wildlife-biological flora characterization (ReGENius)
Automatic assignment and evaluation of your species lists (trees, shrubs, herb layer, ground flora) as well as their wildlife-biological and conservation significance—especially in the context of wild and honey bees. A distinction is made between potential forage value and the popularity of the species present.
At present, ReGENius achieves >95% correct assignment of forage values and nectar/pollen scores. A more in-depth classification is possible for roe deer, red deer, fallow deer, mouflon, chamois, moose, and wild boar. The specialized small game module is currently being developed.
Cut-hair and field-sign identification (ReGENius & ReGENius light)
Support in assigning hair samples and other tracking signs (Pirschzeichen).
The current accuracy is 70–80%. Simply photograph the cut hair on a white tissue.
Kill-site (carcass) identification (ReGENius & ReGENius light)
Support in assigning kills by wolf, lynx, bear, fox, and dog.
Current accuracy is 80–90%, trained with genetically verified or reliably observed kills. Photograph the entire kill site and upload the image.
Wildlife damage
Support in recording and assessing wildlife damage.
This feature is currently still under development. Accuracy is currently max. 50%.
Key Functions
Climate data integration & projections up to 2100 (ReGENius)
Evaluation of regional climate observations and projections for the coming decades, well into the 22nd century—including European climate analogues.
Three forest visions under climate change (ReGENius)
Production forest – High-yield tree species combinations under future climate conditions
Natural forest – Promotion of near-natural structures and biodiversity
Wildlife habitat – Optimization of forage and cover for wildlife
You can subscribe to ReGENius with a monthly, cancellable membership. Simply click on the ReGENius logo—you will be redirected automatically. Access is granted automatically after payment.
All of my AIs were originally trained in German; to use the English version or another language, start the conversation in your native language.
Klick on the icon to use ReGENius
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Please note the following:Automated content
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