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Smart Ag Research

Smart agriculture research is the primary way Olds College tests and validates budding technology to enhance the way producers do agriculture. We take new and developing products and test them according to producer needs, comparing them to proven or established standards and technologies. Smart agriculture research works closely with Olds College Smart Farm to enhance agricultural operations at the College through innovation and the integration of technology on campus, providing unique education opportunities for the next generation of farmers.

Smart Agriculture Research Goals

  • To support producers by providing them with an accurate in-depth analysis of developing technology so they can make informed integration decisions.

  • To evaluate, demonstrate, and validate developing technologies in comparison to pre-established agricultural standards.

  • To improve production efficiency through technology by increasing crop yield and milk production while decreasing waste and used resources.

  

Smart Agriculture Projects

All of our work is centered around advancing the Technology Readiness Level and providing third party validation for developing agricultural technologies.

View Projects

  • Animal Identification using Artificial Intelligence

    Animal Identification using Artificial Intelligence

    Using artificial intelligence for individual animal identification is a novel concept that Olds College is exploring with an industrial client. If successful, producers will be able to identify animals within their herds using images or videos collected from the ground or air, which could have applications to automated illness detection or animal traceability.

    Funder: Industry.

  • Connectivity

    Connectivity

    An interconnected Smart Farm calls for an effectively established network. In partnership with TELUS, ICT International, RealmFive and Tektelic, Olds College is implementing and comparing networks from multiple providers to evaluate their capabilities with the wide array of technologies used on the Smart Farm.

    Funder: Western Economic Diversification, TELUS, Canadian Foundation for Innovation.

  • Corn Grazing

    Corn Grazing

    On the farm, efficiency can be the difference between a productive or a costly season. Using smart technologies in partnership with the UFA, Olds College conducted periodic research into the yield and feed value of different corn varieties to give producers the best chance at lowering feed costs and increasing crop yield.

    Funder: internal funding, with in-kind support from UFA.

  • Decision Support Platform Comparison

    Decision Support Platform

    Data management software is a useful tool when managing on-farm data to make critical management decisions, but the wide selection of systems makes it difficult to know which program would be best suited for a specific application. Olds College uses all platforms, using the same farm data, to provide a full comparison of the costs, performance, features and security that each system offers.

    Funder: internal funding.

  • DOT Autonomous Platform

    DOT Autonomous

    The DOT power platform represents a significant first-step towards autonomous agricultural operations. In partnership with Pattison Liquid Systems and Carlson Agricultural Enterprises, Olds College is conducting future-focused research on the economic, financial, and labour benefits of autonomous equipment for broadacre production.

    Funders and partners: Western Economic Diversification, Canadian Foundation for Innovation, DOT Technology Corp., Raven Industries Inc., Pattison Liquid Systems, Carlson Agriculture Enterprises. 

  • Field to Glass: Barley Trail

    Barley Trail - Field to Glass

    The creation of a brew has an intricate, detailed process that isn’t often seen by the people who drink it. Through our partnerships with Decisive Farming and Grain Discovery, Olds College Brewery has crafted Barley Trail, a fully transparent beer with a scannable QR code that allows the customer to see the entire story from seeding to serving.”

    Funders: internal funding with in-kind contributions from Decisive Farming, Grain Discovery and Red Shed Malting.

  • Grazing Management Project

    Cattle Grazing in a Field

    The grazing management project was established to evaluate and demonstrate the use of various remote grazing management technologies. Olds College is working evaluating the functionality and value of several technologies to help producers understand how these technologies can help increase the efficiency of their intensive grazing systems. The project is also comparing intensive and conventional grazing systems, side-by-side, to quantify the differences on animal, forage, and soil health.

    Funders: Alberta Innovates, Western Economic Diversification, with in-kind contribution from Union Forage.

  • Growsafe

    GrowSafe

    Growsafe is currently developing an in-pasture weighing system prototype  that will provide producers with the ability to monitor their cattle’s weight on a daily basis without the need to handle animals. Olds College is working with Growsafe to validate the technology and measure its effectiveness at weighing cattle, monitoring cattle growth, and taking daily inventory of herds.

  • Heifer Development Program

    Heifer Development

    In Partnership with Neilson Signature Beef, Olds College is seeking to optimize the best practices used in heifer development programs. The project includes measuring the stress levels of heifers acclimated to different levels of human interaction prior to breeding in order to assess the impact on conception rates.

    Funder: NeilsonSignature Beef, NSERC.

  • Hyperlayer Data Concept

    Hyperlayer Data Concept

    The hyperlayer data concept is a process that allows the Olds College Smart Farm to compile, analyse, and use virtually every type of agricultural data. It centers around compiling topographical data, hyperspectral imagery, moisture mapping, yield data, and other information to assist in machine learning for easy analysis and data extraction.

    Funder: Internally Funded