  Currently, weed control in commercial corn production is performed without
considering weed distribution information in the field. This kind of weed
management practice leads to excessive amounts of chemical herbicides being
applied in a given field. The objective of this study was to perform
site-specific weed control (SSWC) in a corn field by 1) using an unmanned
aerial system (UAS) to map the spatial distribution information of weeds in the
field; 2) creating a prescription map based on the weed distribution map, and
3) spraying the field using the prescription map and a commercial size sprayer.
In this study, we are proposing a Crop Row Identification (CRI) algorithm, a
computer vision algorithm that identifies corn rows on UAS imagery. After being
identified, the corn rows were then removed from the imagery and the remaining
vegetation fraction was classified as weeds. Based on that information, a
grid-based weed prescription map was created and the weed control application
was implemented through a commercial-size sprayer. The decision of spraying
herbicides on a particular grid was based on the presence of weeds in that grid
cell. All the grids that contained at least one weed were sprayed, while the
grids free of weeds were not. Using our SSWC approach, we were able to save
26.23\% of the land (1.97 acres) from being sprayed with chemical herbicides
compared to the existing method. This study presents a full workflow from UAS
image collection to field weed control implementation using a commercial-size
sprayer, and it shows that some level of savings can potentially be obtained
even in a situation with high weed infestation, which might provide an
opportunity to reduce chemical usage in corn production systems.

Computer Vision and Pattern Recognition