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Smart Agriculture


As technology continues to influence the way we live, work and farm, smart farms and technology development have a critical role to play in the global grand challenge of feeding a growing population with fewer resources while reducing the environmental impact. 

Technology integration in the agriculture industry is needed to accelerate the progress and innovation to grow Canada’s ag industry. Within broadacre, dryland crop and beef production, applied research is focused on:

  • Automation in ag (autonomous equipment, remote sensing, etc.)
  • Data collection, management and utilization.
  • Validating and assessing ag technologies and best management practices for Western Canadian implementation
  • Assessing and demonstrating technologies and practices that improve environmental sustainability and climate change resiliency in the ag industry.

At Olds College, technology integration on the Smart Farm is divided into three areas of research: Smart Agriculture, Digital Agriculture and Autonomous Agriculture Equipment.

Smart Agriculture

Smart Ag applied research is focused on evaluating, demonstrating and validating agriculture technologies, tools and practices in order to provide manufacturers and users with information on their functionality, accuracy and value — particularly for broadacre, dryland farming in Alberta soil and climate conditions.

The research team collaborates on industry-driven applied research related to smart ag technologies with the goals of saving producers time or money, improving efficiency, and improving environmental sustainability. These technologies include prescription maps, trace gas analyzers, drone and satellite imagery, soil moisture probes, soil nutrient sensors, disease and pest monitoring systems, weather stations, in-bin monitoring systems, rural connectivity solutions and data collection. 

The Smart Ag applied research team is also contracted by companies who need support in validating a recently developed innovative product, technology or process. Data and information collection methods have advanced and are allowing researchers to draw informed conclusions faster to provide better guidance to the agriculture industry.

Highlights of Smart Agriculture Research:

  • The team continues work with Agriculture Financial Services Corporation (AFSC) to see if using drone imagery of hail damaged fields can assist the adjustment process. Additional projects with AFSC include using soil moisture measurements to estimate forage yield potential, and a historical data analysis to learn what variables contribute most to forage growth.
  • Researchers conduct weather station comparisons to help producers identify equipment that would work best for their farms. The team evaluates and audits the stations based on the data collected, add-on options, user platforms and pricing. The team also worked with several different disease models learning about functionality and ease of interpreting the information.
  • The team is exploring variable rate technology alongside TELUS Agriculture with savings, improved yield and reduced environmental footprint as key variables impacting the return on investment. Another project includes monitoring nitrous oxide (N2O), carbon dioxide (CO2) and water vapour (H2O) this growing season with LI-COR chambers installed on the Smart Farm. N2O is a greenhouse gas and researchers are using the chambers to measure emissions from the soil. The technology will help collect high quality (and high resolution) data on how 4R nutrient stewardship practices impact N2O emissions — a highly relevant topic for the ag industry.
  • The Smart Ag applied research team worked with Spornado to evaluate how its innovative wind trap, the Spornado Sampler, can assist producers in making informed fungicide application decisions. Researchers also worked with ChrysaLabs to provide them with a large quantity of soil sampling data for calibration of the ChrysaLabs soil nutrient probe for Western Canada, in addition to evaluating the probe for its usability.
  • Other technologies being tested include equipment to determine carbon content in soil to help farmers access carbon credits, an on-combine NIR (Near Infrared) analyzer for real-time grain constituent analysis, in-bin drying sensors and algorithms to optimize the process and cost of drying, and optical spot-spray technology for reduced input cost and improved environmental sustainability. Connectivity, data collection and communications on the Smart Farm includes extensive 5G, Wi-Fi, LoRaWan and cellular networks to work towards better data integration on the farm.

Digital Agriculture

Smart and precision agriculture are heavily reliant on data, and the Smart Farm prioritizes the collection, integration and utilization of agricultural data for evidence-based decision making to enhance farming decisions.

Ag digitalization represents one of the greatest opportunities — and one of the largest challenges — for agriculture producers. Gathering the right amount of the right information, and then having a way to use it to enhance farming decisions, requires technology that producers can easily understand and manage.

Olds College of Agriculture & Technology has been developing a Digital Ag Strategy that provides guidance for the collection, integration and utilization of agricultural data for evidence-based decision making. This strategy also supports the research and work on the Smart Farm, along with industry partners and the College as a whole.

The College uses advanced digital technologies and tools to enable the collection of millions of data points from individual fields on the Smart Farm. More information helps to understand fields and variability better. These provide training for students, and are used for applied research and the development of new, next generation technologies.

Highlights of Digital Agriculture Research:

  • The HyperLayer Data Concept project is being used to build an extensive look at the Smart Farm. It centers around compiling multiple layers of geospatial information — including topographical data, detailed soil organic matter, nutrient and moisture mapping, multispectral and hyperspectral imagery, yield data, and other layers of information — to assist in machine learning for easy analysis, data extraction and the building of next-generation analytical algorithms.
  • The predictive algorithms developed with this information will be used on-farm to create significant environmental benefits — such as reduced fertilizer and input use, as well as water and other environmental benefits.
  • The team is building a web-based platform to organize, store, manage and process data, as well as machine learning algorithms for predicting plant available soil nutrients, soil organic matter and other field characteristics. Numerous partner organizations see the opportunities of a robust digital agriculture program. The College also collaborates with other post-secondary institutions in the area of data collection and analysis.
  • In addition, Olds College is working with Edmonton-based Wyvern, a space data company, to see what cutting-edge satellite technology could mean for the next chapter in digital innovation in agriculture, and expect the data collected from the Smart Farm to provide solutions related to crop input efficiencies and improved yields.

Autonomous Agriculture Equipment

Olds College of Agriculture & Technology is conducting future-focused research on the evaluation and improvement of economic, environmental, and logistical benefits of autonomous agricultural equipment for broadacre crop production.

The Smart Farm is on its fourth consecutive growing season using the Raven OMNiPOWER™ platform for significant seeding, spreading and spraying duties. Over three years of research with autonomous farming equipment has helped the team run the equipment more efficiently, get more acreage coverage, and improve field efficiencies and uninterrupted hands-off operation. As team members continue to gather more and more data during research activities, they gain more insights into the performance of autonomous equipment on the farm. While OMNiPOWER operates on its own after a mission is programmed, it requires supervised autonomy which means it must stay within line-of-sight of the team.

The Smart Ag research team also started the 2023 growing season with a brand new OMNiPOWER 3200 platform — a gift-in-kind from Raven Industries, Inc. — utilizing technology and equipment to farm more efficiently. Using the OMNiPOWER 3200, researchers are planning to get an increased amount of acreage coverage, expand data collection to further improve efficiencies with autonomous equipment and map cellular connectivity in real time. 

Olds College students also receive hands-on learning opportunities by operating, studying and using data from OMNiPOWER on the Smart Farm and in the classroom. The precision agriculture programs at the College, and the inclusion of OMNiPOWER and autonomy in student learnings, is getting students ready to work in the ag tech industry.

Highlights of Autonomous Agriculture Equipment Research:

  • Raven loaned Olds College a 2020 OMNiPOWER with a Seedmaster 30-foot air seeder implement for the 2023 seeding season — giving the researchers access to two OMNiPOWER platforms on the Smart Farm. Having two platforms allowed researchers to operate both the OMNiPOWER 3200 and the 2020 OMNiPOWER at the same time in the same field. The learnings and data collection from this opportunity is game-changing for autonomous operations.
  • Another project milestone was comparing autonomous equipment operations to conventional equipment in terms of cost, labour and efficiencies. Team members working with OMNiPOWER perform comparable autonomous data collection with an electronic data collection system called a Somat-eDAQ. The device electronically collects location specific data (GPS) and equipment data (CAN bus) at a rate of two times a second and includes starts, stops, and field and fuel efficiency. The College owns two Somat-eDAQ devices: one is installed on OMNiPOWER and the second is housed in a carrying case and used to collect data in conventional equipment. This provides the team with robust datasets used to evaluate autonomous versus conventional equipment.
  • The team also tested the OMNiPOWER-ready coulter toolbar for liquid sectional control from Pattison Liquid Systems to reduce on-farm input costs. Learnings from operating this equipment in a new region and soil zone on the Smart Farm were passed on to Pattison after the trial period.
autonomous farm vehicle in field with Olds students

Smart Ag Research Projects

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Current Projects

Reducing Seed Loss

Olds College Centre for Innovation (OCCI) is working on a project to help reduce seed loss in straight-cut canola operations.

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Agtron Remote Monitoring

Seed monitoring systems can be a useful tool for producers, helping mitigate problems like planting too much seed or not having enough seed.

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AgExpert Field

Olds College Centre for Innovation (OCCI) is teaming up with AgExpert and FCC to evaluate farm accounting software using data from the Olds College Smart Farm. 

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Combyne Ag

Olds College Centre for Innovation (OCCI) is teaming up with Combyne Ag to evaluate its software for farmers.

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Axa Remote Sensing

Precision agriculture throughout Western Canada is rapidly growing, but adopting precision agriculture technologies — like variable rate application — is low, according to experts. 

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On-Farm Precision Experimentation

Olds College Centre for Innovation (OCCI) and Alberta Grains are collaborating with producer partners to introduce a new on-farm research method in central Alberta. The project uses the On-Farm Precision Experimentation methodology defined by Data-Intensive Farm Management, focusing on different wheat seed and nitrogen rates over two years.

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Threshold UAV Drone Rock Mapping

Olds College Centre for Innovation (OCCI) along with Threshold UAV will be flying multiple drones at the same time (swarm drones) on the Olds College Smart Farm to locate rocks. The processed drone data will be used to create a rock map of the field. OCCI will verify the rock map created via field scouting to validate the use of swarm drones for rock mapping.

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Fendt & Precision Planting Canola Trials

Olds College Centre for Innovation (OCCI) is working alongside Trochu Motors and Precision Planting to compare the agronomic and economic impact of using a precision planter compared to a conventional air seeder during canola seeding in a field trial.

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Remote Data Technology

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.

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Disease Distribution in Canola

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.

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Best Management Practices for Converting Marginal Farmland

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.

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Remote Sensing for Vegetation Assessments in DSAs

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).

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BioScout - Identify & Quantify Disease Spores

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. 

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LI-COR Chambers - N2O Emissions Small-Plot Trials

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.

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Autonomous Agriculture Equipment

The Raven OMNiPOWER™ platform represents a significant first-step towards autonomy applied to agricultural operations.

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Bio-Agtive Emissions Farming Small Plot Trials

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.

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Bushel Plus Harvest Loss Monitoring

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.

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Comparable Autonomous Data Collection

Team members are performing Comparable Autonomous Data Collection with an electronic data acquisition instrument called Somat-eDAQ.

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Comparisons of In-Field MicroClimate Variability & External Weather Stations

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.

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Disease Presence in Relation to Soil Characteristic

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.

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Evaluation of the MINS Process for Carbon Credits

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.

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Exploring Soil Moisture, Weather & Forage Biomass Relationships

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.

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Hail Imagery Research

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.

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In-Bin Drying: Top Grade Ag

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.

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Saskatchewan Smart Farm Field Data Collection

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.

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Variable Rate Technology Economic Modelling

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.

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Weather Station Array

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.

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HyperLayer Data Concept

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.

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Team Members
Angie Stoute headshot

Angie Stoute

Research Technician

Sofia Bahmutsky headshot

Sofia Bahmutsky

Data Scientist

Akshay Bhanot headshot

Akshay Bhanot

Software Developer & Data Scientist

Blair Bateman headshot

Blair Bateman

Research Technician

Julie Cobb headshot

Julie Cobb

Research Technician

Eli Anderson headshot

Eli Anderson

Research Technician

Felippe Karp headshot

Felippe Karp

Research Associate

Jyothish Prabhakaran Jayasree headshot

Jyothish Prabhakaran Jayasree

Software Developer & Data Scientist

Christina Kaye headshot

Christina Kaye

Project Lead

Roy Maki headshot

Roy Maki

Research Project Manager

Yevgen Mykhaylichenko headshot

Yevgen Mykhaylichenko

Technology Integration Specialist

Chris Ouellette  headshot

Chris Ouellette

Research Technical Lead

Abby Sim headshot

Abby Sim

Project Lead

Herman Simons headshot

Herman Simons


Ashutosh Singh headshot

Ashutosh Singh

Data Scientist

Daniel Stefner headshot

Daniel Stefner

Project Lead

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Herman Simons
Manager - Smart Agriculture

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