Canada’s grain sector is a key economic driver, contributing over $20 billion in wheat export sales annually. Product quality is a critical component of the grain value chain, impacting everyone from producer to consumer. Evaluation is the responsibility of grain inspectors, who must manually identify, separate, and analyze degrading kernels to determine a sample’s quality and grade. These subjective results can be unreliable and inaccurate, and may result in conflict between the buyer and seller, damaging important commercial relationships. For many years, the industry has sought an affordable solution capable of delivering a quick and accurate end-use quality assessment based on representative samples.
In the first of the project’s three phases, the team of agri-food companies and academic institutions employed diverse technologies to develop a novel geospatial artificial intelligence (GeoAI) platform proof-of-concept that automates manual wheat-production observations. The GeoAI team leveraged geospatial, deep learning, machine vision, and high-performance computing technology to evaluate three representative primary objective characteristics and one subjective characteristic in Canada Western Red Spring Wheat (CWRS) kernels.
Phase 2 will launch commercial applications for wheat producers and grain buyers, with more robust datasets and higher accuracy for five primary objectives (Ergot, Fusarium Damaged Kernels, Hard Vitreous Kernels, Sprout, and Sawfly) and two subjective grading factors (frost/heat stress damage and Green) in CWRS kernels. Phase 3 is being refined.
The project team’s goal is to create and market a scaled-up, all-in-one GeoAI-driven cloud platform that automates numerous agricultural tasks, with a focus on grain grading. This will reduce manual observation requirements, increasing productivity, profitability, sustainability, and competitiveness for Canadian producers, which aligns with CAAIN’s mandate to advance the agri-food sector by supporting advances in automation & robotics, data-driven decision-making, and smart farm platforms.
Total Project Value