Abstract
Management of nitrogen (N) fertilizers is an important agricultural practice and field of research to minimize environmental impacts and the cost of production. To apply N fertilizer at the right rate, time, and place depends on the crop type, desired yield, and field conditions. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral imagery, vegetation indices (VI), crop height, field topographic metrics, and soil properties to predict canopy nitrogen weight (g/m2) of a corn field in southwestern Ontario, Canada. Random Forests (RF) and support vector regression (SVR) models were evaluated for canopy nitrogen weight prediction from 29 variables. RF consistently had better performance than SVR, and the top-performing validation model was RF using 15 selected height, spectral, and topographic variables with an R2 of 0.73 and Root Mean Square Error (RMSE) of 2.21 g/m2. Of the model’s 15 variables, crop height was the most important predictor, followed by 10 VIs, three MicaSense band reflectance mosaics (blue, red, and green), and topographic profile curvature. The model information can be used to improve field nitrogen prediction, leading to more effective and efficient N fertilizer management.
Highlights
Agriculture is an important industry as the basis of food security, and as a significant aspect of the world economy
In Canada, where agriculture is a significant industry, developing agricultural methods to be adaptable and resilient is necessary [1]. This is possible through precision agriculture (PA), a management technique that selectively applies crop farming resources such as fertilizer, water, pesticides, and herbicides based on the plant needs within a field [2,3,4]
During July to August, corn crops reached middle to late growth stages (BBCH 50+) and application of nitrogen fertilizer is not recommended after plants begin tasselling [6]
Summary
Agriculture is an important industry as the basis of food security, and as a significant aspect of the world economy Factors such as rapidly increasing global demand, fluctuations in production due to climate change, and a greater awareness of the negative environmental impact of agriculture on surrounding ecosystems, contribute to an increasing need for more efficient and sustainable farming practices. In Canada, where agriculture is a significant industry, developing agricultural methods to be adaptable and resilient is necessary [1] This is possible through precision agriculture (PA), a management technique that selectively applies crop farming resources such as fertilizer, water, pesticides, and herbicides based on the plant needs within a field [2,3,4].
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