Digital agriculture: Non-destructive Phenotyping and Disease Symptom Monitoring using Remote Sensing

Agriculture must meet the needs of the continuously growing world population. To this end, sustainable strategies that improve resource use efficiency (human, fuel, water, herbicides, pesticides, and fertilizers), reduce inputs and environmental impacts, and sensing methodologies that provide actionable information are in demand. This research will increase the potential for collecting high-quality data in the field to break the data bottleneck and automate data analysis processes to conduct routine biological investigations and phenotypic predictions. The project aims to develop a digital scouting method that enables farmers to conduct routine biological investigations and phenotypic predictions of crops using remote sensing.

Faculty Supervisor:

Muditha Heenkenda

Student:

Partner:

Universidad Nacional Autónoma de México

Discipline:

Earth science

Sector:

Education

University:

Lakehead University

Program: