Spot weed management in soybean production remains a major agronomic challenge, and conventional broadcast herbicide applications can result in unnecessary treatment of weed-free areas, increased costs, and promote herbicide resistance in weeds. Unmanned Aerial Vehicle (UAV) remote sensing is widely used to monitor and map weed distribution for prescription-based or blanket-rate herbicide applications, but adaptive aerial spot-spraying systems that detect weed spots and onboard herbicide applications are still being developed. The objective of this study was to develop an adaptive spot-spraying UAV system that quantifies image vegetation coverage and uses vegetation exceeding a soybean-only reference baseline as a proxy for localized weed pressure to support on-the-go spray decisions. Soybean canopy images were collected from 20 field plots under weed-free and weed-infested conditions 14 and 24 days after planting (DAP). An automated workflow was deployed on a UAV-mounted subsystem consisting of a minicomputer, a multispectral camera, a pump, and paired spray nozzles to apply herbicide according to the vegetation coverage. In weed-free plots, soybeans grew by 10.63% in vegetation coverage between 14 and 24 DAP while the coefficients of variation were greater than 40% which indicated high variability across images. Indoor testing with simulated crop and weed pressure closely tracked with reference values, with R² = 0.91, MAE = 8.4%, and RMSE = 9.9%. Field vegetation coverage was strongly correlated with reference measurements, yielding R² = 0.69, MAE = 2.2%, and RMSE = 2.9%. Field evaluation focused on vegetation-coverage estimation, while spray-actuation performance was evaluated under controlled indoor conditions. Automated spray actuation achieved 97.3% overall accuracy. UAV-based quantification of vegetation provides a foundation for future aerial systems with actuation, enhancing precision weed management, reducing soil compaction, and impeding herbicide resistance.
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Azmir et al. (2026) studied this question.
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