Intra-field Canopy Nitrogen Retrieval from Unmanned Aerial Vehicle Imagery for Wheat and Corn Crops in Ontario, Canada

The optimization of crop nitrogen fertilization to accurately predict and match the nitrogen (N) supply to the crop N demand is the subject of intense research due to the environmental and economic impact of N fertilization. Excess N could seep into the water supplies around the field and cause unnecessary spending by farmers. Understanding the detailed spatial information about a crop status is known as a farming management technique called precision agriculture, which allows farmers to maximize their yield and profit while reducing the inputs of fertilizers, pesticides, water, and insecticides. The goal of this study is to document and test the applicability and feasibility of using Unmanned Aerial Vehicle (UAV) to predict nitrogen weight of wheat and corn fields in south-west Ontario. This is investigated using various statistical modelling techniques to achieve the best accuracy. Machine learning techniques such as Random Forests and Support Vector Regression are used, which provide more robust models than traditional linear regression models. The results demonstrate that most spectral indices have a non-linear relationship with canopy nitrogen weight and show high degree of multicollinearity among the variables. In this thesis, the final nitrogen prediction maps of wheat and corn fields using UAV images and the derived models are provided