Robotic Weed Management in Flax and Canola

Who is working on this project: Shivam Panda, Yongkyu Lee

 

The purpose of this page is to record and share the findings of our research project titled “Autonomous Robotic Systems for Precision Weed Control in Flax”, supported by USDA. We are in collaboration with researchers from North Dakota State University to achieve our goals. Our aim is to design and develop autonomous mobile robots equipped with highly efficient spray solutions, to manage weed in flax fields.

This page will be continually updated to reflect current progress.

 

The objective of this project can be summarized with the following keywords:

  • Precision

  • Seamless travel in the field

  • Low-cost

  • Autonomy

  • Onboard Intelligence

  • Scalability

  • Enhanced herbicide formulation

 

Here are some videos that highlight the robot’s capabilities

Demonstration of the robot navigating across rows of flax and spraying.

Timelapse of the robot in action.

Robot navigating back to the charging station.

Yayun describing the robot to our Lab guests, Swapnil and Sandeep.

 

News

  1. Meet Arthur Lovekin!

    Arthur is a fourth-year undergraduate student, majoring in Mechanical Engineering. He has been deeply involved in the development of the robot, from computer-vision-based navigation to hardware design. He spent the summer of 2021 with Yayun Du in the flax fields of Fargo, North Dakota, to conduct experiments and gather data. Through the UCLA SPUR program (Summer Programs for Undergraduate Researchers) and REU (Research Experience for Undergraduates), he was able to get hands-on experience as an undergraduate researcher, which helped him navigate through research and prepare for graduate school. He claims the key to his successful experience was staying self-motivated, which led him to want to learn more. He was able to develop practical skills in ROS, electronics, and computer programing in various languages during the project.

 

Publication: Du, Y., Zhang, G., Tsang, D. and Jawed, M.K. (2022). Deep-CNN based Robotic Multi-Class Under-Canopy Weed Control in Precision Farming. [online] IEEE Xplore. [LINK]

Du, Y., Mallajosyula, B., Sun, D., Chen, J., Zhao, Z., Rahman, M., Quadir, M. and Jawed, M.K. (2021). A Low-cost Robot with Autonomous Recharge and Navigation for Weed Control in Fields with Narrow Row Spacing. [online] IEEE Xplore. [LINK]

Funding: We are grateful for the support from USDA (Award # 2021-67022-34200)

GitHub: TBD

YouTube: https://youtu.be/AOYxt7r6_vU https://youtu.be/9e3Q_9aTCQ4