  Precise robotic weed control plays an essential role in precision
agriculture. It can help significantly reduce the environmental impact of
herbicides while reducing weed management costs for farmers. In this paper, we
demonstrate that a custom-designed robotic spot spraying tool based on computer
vision and deep learning can significantly reduce herbicide usage on sugarcane
farms. We present results from field trials that compare robotic spot spraying
against industry-standard broadcast spraying, by measuring the weed control
efficacy, the reduction in herbicide usage, and the water quality improvements
in irrigation runoff. The average results across 25 hectares of field trials
show that spot spraying on sugarcane farms is 97% as effective as broadcast
spraying and reduces herbicide usage by 35%, proportionally to the weed
density. For specific trial strips with lower weed pressure, spot spraying
reduced herbicide usage by up to 65%. Water quality measurements of
irrigation-induced runoff, three to six days after spraying, showed reductions
in the mean concentration and mean load of herbicides of 39% and 54%,
respectively, compared to broadcast spraying. These promising results reveal
the capability of spot spraying technology to reduce herbicide usage on
sugarcane farms without impacting weed control and potentially providing
sustained water quality benefits.
