GeoAI Platform for Automating Manual Observation Associated with Wheat Production

Canada’s grain sector is a key economic driver, contributing over $20 billion in wheat export sales annually. Product quality is 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 analyse degrading kernels to determine a sample’s quality and grade. These subjective results can be unreliable, 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.

This project team of agri-food companies and academic institutions will employ diverse technologies to develop a novel geospatial artificial intelligence (GeoAI) platform proof-of-concept that automates manual wheat-production observations. The Super GeoAI Technology team will leverage 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 kernels.

Following the initial 15-month CAAIN investment period, R&D will continue, leading to eventual commercialisation and product adoption. The 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 grain producers.

CAAIN Contribution
$372,370

Total Project Value
$1,198,103

Project Contact
Weiping Zeng
Super GeoAI Technology
wzeng@sga.ai