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Smart Agriculture

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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Team Members
Sofia Bahmutsky headshot

Sofia Bahmutsky

Data Scientist

Akshay Bhanot headshot

Akshay Bhanot

Software Developer & Data Scientist

Julie Cobb headshot

Julie Cobb

Research Technician

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

Matilda Schmohl headshot

Matilda Schmohl

Research Technician

Abby Sim headshot

Abby Sim

Research Technician

Herman Simons headshot

Herman Simons


Ashutosh Singh headshot

Ashutosh Singh

Data Scientist

Daniel Stefner headshot

Daniel Stefner

Project Lead & Farm Liaison

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Herman Simons
Manager - Smart Ag

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