Variable management of fertilizer in canola has consequences with respect to economic return and environmental impact.

The TerraByte project at UWinnipeg has several goals to improve and develop digital ag: 1) the development of large databases of plant and weed images that will be open to Canadian researchers and industry, (2) the development of machine learning (ML) models for plant identification and classification that will be useful for automated methods of phenotyping/breeding, disease detection, weed detection, etc.; (3) the development of new imaging techniques and sensors that can assist with these tasks, (iv) the development of an automated data rover to collect high volumes and quality of such data in test plots; (5) extensive academic and industry partnerships to move the aforementioned toward real world use and commercialization.

NMR and MASS Spectrometry analysis of Protein based foods and biological samples foloowing the consumption of foods