Olds College Centre for Innovation (OCCI) researchers are evaluating different solutions for the Olds College Smart Farm to collect equipment data for field operations with different manufacturers remotely.
In collaboration with xarvio Digital Farming Solutions, Olds College Centre for Innovation (OCCI) is completing disease identification on Field 18 of the Olds College Smart Farm to display canola disease distribution within the field.
Olds College Centre for Innovation (OCCI) is working with Farm Credit Canada (FCC) to assess best management practices (BMPs) for the removal of marginal cropland from annual production.
Olds College Centre for Innovation (OCCI) partnered with Ember Resources Inc. on a multi-year project to determine if remote sensing technologies could be used for the vegetation assessment component of a Detailed Site Assessment (DSA).
Pan-Canadian Smart Farm Network members are conducting a multi-year project with BioScout to sample this revolutionary technology and help improve Bioscout for use in Western Canadian agriculture.
LI-COR chamber technology will help OCCI collect high quality and high resolution data on how 4R (right rate, right time, right source, right place) nutrient stewardship practices impact N2O emissions — a highly relevant topic for the ag industry.
The Raven OMNiPOWER™ platform represents a significant first-step towards autonomy applied to agricultural operations.
The Bio-Agtive Method is an innovative bio agtech approach which entails emission capture technology and re-purposing of biofuel exhaust emissions into a carbon-based biofertilizer.
Bushel Plus provides a complete combine loss measurement system designed to quickly and accurately quantify harvest loss to assist in the calibration of combine settings during harvest.
Team members are performing Comparable Autonomous Data Collection with an electronic data acquisition instrument called Somat-eDAQ.
Pan-Canadian Smart Farm Network members are conducting a second year of research to compare data collected from weather sensors inside and outside of the crop boundary to produce multiple data sets for analysis, and evaluating how disease development varies within each zone.
Olds College Centre for Innovation (OCCI) is performing intensive one-acre grid scouting for the purpose of identifying disease variability within a field, and comparing the distribution of disease results to other geospatial layers collected within the HyperLayer project.
Carbon Assets Solutions (CAS) is a new Canadian company that uses Mobile Inelastic Neutron Scattering (MINS) technology to determine carbon content in soils that may help producers access carbon credits.
Olds College is performing a historical data correlation analysis in partnership with Agriculture Financial Services Corporation (AFSC) to learn from existing data sources what variables matter most to forage growth.
Olds College and Agriculture Financial Services Corporation (AFSC) are continuing their work together to determine if high definition imaging from unmanned aircraft vehicles (drones) can be used to classify hail damaged areas within crop fields.
The In-Bin Drying Monitor is a technology developed by Top Grade Ag that uses a proprietary algorithm with pressure, temperature and humidity sensors to optimize grain drying procedures.
Olds College is working with local Saskatchewan partners to gather historical and current information from the fields on the Saskatchewan Smart Farm for baseline data collection.
Olds College is working with TELUS Agriculture on assessing the return on investment of variable rate technology (VRT) which is a precision-ag approach allowing producers to manage defined areas of their fields differently.
The Weather Station Array provides a comparison of functionality, platform navigation, installation/uninstallation, reliability of data, user experience, and connectivity of commercially available weather stations.
The HyperLayer Data Concept is a process that allows the Olds College Smart Farm to compile, analyze, and use virtually every type of agricultural data.