The estimation and prediction of crop biomass and final yield using UAV-based point cloud data

This study will focus on an alternative remote sensing method for crop long-term biomass monitoring and prediction of final yield using Unmanned Aerial Vehicle (UAV) based 3D point cloud data in Southwestern Ontario. Currently, biomass and yield are estimated from statistical and crop growth models. However, statistical models are only applicable for specific area or environmental conditions; crop growth models require many input parameters which are impractical for individual farmers. The allometric method could be an alternative for crop biomass estimation. Moreover, the key parameter, crop height, is difficult to obtain from satellite or airborne remote sensing data. According to the outcomes from my previous study, crop canopy height and LAI estimation methods using the UAV-based point cloud data could be valuable information on crop biomass estimation using the allometric method.

Faculty Supervisor:

Jinfei Wang

Student:

Yang Song

Partner:

A&L Canada Laboratories Inc

Discipline:

Geography / Geology / Earth science

Sector:

Agriculture

University:

Western University