Canadian PI: Dr. Nazir Kherani                  

Canadian Institution: University of Toronto

Indian PI: Dr. Prashant Mishra

Indian Institution: Indian Institute of Technology (IIT) Delhi

Project Summary:

Food security is an emergent global issue considering the growth in human population – rising from 8 billion currently to 10 billion by 2050 – and commensurate elevation in the standard of living. Moreover, food supply concerns have been exacerbated in the post-COVID and Russian-Ukraine conflict era owing to supply chain issues and geopolitical upheaval effects, respectively. Hydroponic greenhouses have steadily evolved into a viable means of farming where water utilization efficiency is greater than 90% and crop productivity is markedly increased, hence making hydroponic greenhouses a compelling approach to meet a critical share of global agricultural production. Despite their advantages, hydroponic greenhouses have a significant vulnerability: pathogens. For example, tomato brown rugose brown fruit virus (ToBRFV), a new pathogen from the tobacco and tomato mosaic viruses family, has been rapidly spreading in Canada (starting in Ontario) since 2019. It has caused crop losses of 30% to 70% in tomatoes and peppers. Even though ToBRFV has not been officially reported in India, the tomato and pepper seeds imported from India to North America and some European countries have been found to be contaminated with ToBRFV.

Agricultural operations are largely flying blind in this battle against pathogens. Current techniques for pathogen screening have been inefficient, practically and cost-wise, to prevent spread of disease in greenhouses. They lack vigilance, sensitivity and continuous local monitoring capabilities. The objective of this project is to demonstrate an ultra-sensitive, highly accurate and rapid point-of-plant-care pathogen contamination sensing system for early-stage detection of incipient pathogens in greenhouses. Viral loads appear in the roots of plants at the beginning of disease development. Hence, as a proof-of-concept, detection of biomarkers for tomato and cucumber mosaic viruses, and pepper mild mottle virus doped in clean water samples will be demonstrated. The detection method is based on two novel approaches: 1- multiwavelength label-free surface-enhanced Raman spectroscopy integrated with machine learning and 2- molecularly imprinted polymer functionalization followed by detection based on field effect transistor. These two methods complement each other and the results will be ensembled to increase sensing accuracy. These techniques offer unprecedented sensitivity, accuracy, multiplexing, and repeatability in detection.