I know what you grew last summer | Steve Shirtliffe | TEDxUniversityofSaskatchewan

Quick Overview

Dr. Steve Shirtliffe advocates for integrating Artificial Intelligence (AI) and satellite imagery into modern precision agriculture practices to improve crop phenotyping, manage variable field conditions like salinity and weed patches (like Kochia), and ultimately increase yield efficiency while protecting farmers' livelihoods and the environment.

Key Points: Dr. Shirtliffe, a digital agronomist, uses historical spy satellite imagery from 1982 to illustrate the long-term variability in his family's Southern Manitoba farm, which was impossible to control then. The core focus is using AI and freely available satellite imagery (like Google Earth Engine data) to perform high-resolution phenotyping, identifying crop characteristics like growth rate and head count. A key application demonstrated is the detection of Kochia (a weed resistant to glyphosate) using satellite imagery, allowing farmers to target weed patches before they spread seed. The research also involves creating productivity maps (four-year average) classifying fields as 'Stable high,' 'Stable low,' or 'Unstable' based on factors like soil salinity and wetness, enabling variable management prescriptions. The ultimate goal is to increase farming efficiency by tailoring inputs (fertilizer, etc.) to specific management zones within a field, rather than treating the entire field uniformly. Shirtliffe emphasizes that this technology must be used ethically, benefiting farmers and food security, not exploiting them or negatively impacting commodity prices or national security interests.

Context: Dr. Steve Shirtliffe, a digital agronomist from the University of Saskatchewan, presents his work exploring the application of Artificial Intelligence (AI) and remote sensing, specifically satellite imagery, to revolutionize precision agriculture. He grounds his modern research in personal history, referencing aerial photos of his family farm in Southern Manitoba taken during the Cold War era (1982) to establish the long-term challenge of managing inherent field variability.