Grainswest - Tech 2024

Tech 2024 grainswest.com 35 IN THE WEEDS WITH AI Geco Strategic Weed Management has built two AI-equipped digital tools intended to improve weed management. CEO Greg Stewart has worked as a control engineer to improve digital and automated processes in the manufacturing and oil and gas industries. In recent years, he noticed the rise of data technology in agriculture and liked the prospect of helping farmers tackle uncertainty and feed the world. In 2019, he joined Ecoation, an AI-driven greenhouse software company. Turning to broadacre crops, he launched Geco Engineering. With data sources such as satellite and drone imagery and precision ag platforms, the company creates predictive weed maps for farmers and agronomists. Its two products have been available since August 2023 and as of this spring it had 50 farm clients. The Geco weed prediction system employs AI to model the activity of a weed population across five years. It considers weed location and biological details, cropping history and soil type. The system uses this to disrupt the weed’s lifecycle with the aid of prescription maps and a choice of three levels of management aggressiveness. The farmer uploads the preferred map to their granular applicator, seed drill or sprayer. The system also generates a five-year weed history report that can be used as a further decision- making resource. “Many customers use our predictive maps to target soil- applied, residual herbicide on the weed hot spots,” said Stewart. These products can be effective as their modes of action differ from foliar chemistries. They are also expensive, so targeting established hot spots rather than blanket application is cost effective. Stewart used the ballpark comparison of $30 per acre versus $10 with a targeted approach. A farmer can also choose to fight hot spots with a non-chemical practice such as increased seeding rate. Stewart noted the extreme example of a client who seeded corn into weed hot spots in canola, which pushed those money-losing acres into the black. The weed prediction system can complement the use of an optical spot sprayer, another emerging technology, said Stewart. The sprayer collects data that captures weed emergence on a given day, and this can be used by the prediction system to sharpen its long-term modelling. In turn, Geco’s AI models can predict the volume of chemistry the sprayer will require. With the use of slightly different data, Geco also offers a herbicide resistance early detection service. Historically, weed resistance has been identified at about 30 per cent prevalence, said Stewart. The tool models weed behaviour to earlier identify and map small patches that behave abnormally. As well, a farmer can scale a suspected resistance problem down from, say, a 10,000-acre scouting chore to more manageable five- to 10-square-metre patches. If this precise scouting finds signs such as plants older than the last spray application, management strategy can pivot accordingly. Sampling these sites for lab analysis also increases the likelihood resistant weeds will be found before they multiply exponentially. Having gotten its start on the Prairies, Geco is now exposing its system to as many crop, geography and weed combinations as possible, said Stewart. This includes Eastern Canada and the U.S. Midwest. “The algorithm will improve as we work on this over time.” Hinting at the broader potential of AI to enhance agriculture, is Geco’s “frictionless adoption.” The tools do not require new equipment or practices, and a farmer can employ a prescription map within a day of signing up. With the use of AI, big data and equipment such as optical spot sprayers on the rise, five years from now, weed control will approach its optimal potential, Stewart believes. “We’re on a really interesting and exciting journey.” The Geco weed prediction system uses AI-equipped digital tools to disrupt the weed lifecycle. Image:CourtesyofGecoStrategicWeedManagement