Geco Engineering develops agronomic models that utilize data from farm equipment and remote sensing to create predictive weed maps and identify areas at risk of herbicide resistance. This technology enables farmers to target weed hotspots effectively, optimizing herbicide application and improving crop management.
Funding
Funding not disclosed
Founders
Product
Problem
Farmers face challenges in effectively managing weed infestations, leading to yield losses and increased herbicide costs. Traditional weed control methods often involve blanket herbicide applications, which can be environmentally damaging and contribute to the development of herbicide-resistant weeds. Identifying and targeting weed hotspots accurately is difficult, especially when weeds are obscured by crops or soil.
Solution
Geco Engineering provides agronomic models that leverage data from farm equipment and remote sensing to generate predictive weed maps and identify areas at high risk of herbicide resistance. By analyzing field data, Geco's technology enables farmers to precisely target weed infestations, optimizing herbicide application and minimizing environmental impact. The system delivers actionable insights, including customized spray maps for targeted herbicide application and risk maps to guide scouting efforts for herbicide-resistant weeds. This approach allows for more efficient and sustainable weed management practices, reducing herbicide usage and improving crop yields.
Target Audience
Geco Engineering's primary customers are North American farmers seeking to improve weed management practices, optimize herbicide application, and mitigate the risk of herbicide resistance.
Features
- Predictive weed maps generated from farm equipment data and remote sensing imagery
- Identification of areas with high risk of herbicide resistance
- Customized spray maps for targeted herbicide application
- Risk maps to guide scouting efforts for herbicide-resistant weeds
- Agronomic models that integrate various data sources for comprehensive weed management