Weed-Spraying Robot

Research Paper: Y. Du et al., “Neural-Kalman GNSS/INS Navigation for Precision Agriculture,” 2023 IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, 2023, pp. 9622-9629, doi: 10.1109/ICRA48891.2023.10161351.

Weed-Spraying Robot

In the summer of 2021, I worked with Yayun Du from the Structures-Computer Interaction (SCI) Lab at UCLA to create a robot that could spray herbicide on flax plants in Fargo, North Dakota. Our goal is autonomous, precision farming: allowing robots to meticulously target individual weeds instead of entire fields, thereby dramatically reducing the amount of herbicide and human labor needed to grow crops. Our robot utilizes computer-vision-based navigation and weed-identification algorithms in order to spray weeds with high efficiency, and offers an inexpensive alternative to larger industrial robots designed for large row spacings. The robot identifies flax plants using an implementation of the YOLOv5 neural network, and uses the plant locations both to navigate and to spray weeds (though these algorithms are still under development).


[July 7] Piggybot
[July 22] Franky
[August 2] Jetbot
[August 16] Turtlebot


Weeds are successfully killed in the center of rows, but they persist in the areas that the robot cannot access without trampling the flax. Hardware upgrades to address this, including a more adaptable spraying system with wider range, are under development.


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[2] “TerraSentia.” EarthSense, www.earthsense.co/. 

[3] Du, Yayun, et. al. “A Low-Cost Robot with Autonomous Recharge and Navigation for Weed Control in Fields with Narrow Row Spacing.” International Conference on Intelligent Robots and Systems. 

[4] Jocher, G. (2020, May). GitHub – ultralytics/yolov5. GitHub. https://github.com/ultralytics/yolov5


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