Precision Agriculture
Optimization of nitrogen (N) management in agriculture is key to addressing economic and environmental issues associated with N fertilization. The presence of in-field spatial variability makes the task more challenging. Therefore, it is important to be able to detect variability in crop N status within a field. A strong relationship between total chlorophyll content in a maize canopy and the crop N status has been well established. Recent research into detecting crop N status has focused on non-destructive sampling techniques. Non-destructive techniques focus on remote sensing to correlate with and quantify canopy chlorophyll content. Studies have suggested different strategies for in-season N management using remote sensing that monitor differences in crop N status by evaluating relative crop response to applied N. In-season N application practices guided by canopy sensors are yet to be explored in many crops grown in semi-arid western Nebraska.
Crop Sensing
This lab conducts crop sensor experiments on varieties of crops such as winter wheat, sugar beet, millet, dry edible bean, etc.
This image shows us Normalized Difference Vegetation Index (NDVI) values in a bean field at the Panhandle, Research, Extension & Education Center. The remote sensing image was taken by a drone mounted Altum Micasense multispectral sensor.
The NDVI is a remote sensing metric used to measure plant health and density by analyzing the differences between near-infrared (NIR) and red light (Stamford et al. 2023). Ranging from -1 to 1, higher values indicate healthy, dense vegetation, while lower values suggest stressed or dormant plants. These types of images will be used for crop health monitoring, irrigation and drought management, deforestation and land cover change, precision agriculture, and growth modeling.
