Skip to main content

ORIGINAL RESEARCH article

Front. Robot. AI
Sec. Industrial Robotics and Automation
Volume 11 - 2024 | doi: 10.3389/frobt.2024.1441371
This article is part of the Research Topic Intelligent Robots for Agriculture -- Ag-Robot Development, Navigation, and Information Perception View all 3 articles

Targeted Weed Management of Palmer Amaranth using Robotics and Deep Learning (YOLOv7)

Provisionally accepted
Amlan Balabantaray Amlan Balabantaray *Shaswati Behera Shaswati Behera Cheetown Liew Cheetown Liew Nipuna Chamara Nipuna Chamara Mandeep Singh Mandeep Singh Amit J. Jhala Amit J. Jhala Santosh Pitla Santosh Pitla
  • University of Nebraska-Lincoln, Lincoln, United States

The final, formatted version of the article will be published soon.

    Effective weed management is a significant challenge in agronomic crops which necessitates innovative solutions to reduce negative environmental impacts and minimize crop damage. Traditional methods often rely on indiscriminate herbicide application, which lacks precision and sustainability. To address this critical need, this study demonstrated an AI-enabled robotic system, Weeding robot, designed for targeted weed management. Palmer amaranth (Amaranthus palmeri S. Watson) was selected as it is the most troublesome weed in Nebraska. We developed the full stack (vision, hardware, software, robotic platform, and AI model) for precision spraying. Using YOLOv7, a state-of-the-art object detection deep learning technique, the Weeding robot achieved an average of 60.4% precision and 62% recall in real-time weed identification and spot spraying with the developed gantry-based sprayer system. Weeding robot successfully identified Palmer amaranth across diverse growth stages in controlled outdoor conditions.

    Keywords: weed management, Robotics and Automation in Agriculture, Machine Vision, artificial intelligence, Targeted spraying, real time, deep learning, YOLOv7

    Received: 30 May 2024; Accepted: 26 Sep 2024.

    Copyright: © 2024 Balabantaray, Behera, Liew, Chamara, Singh, Jhala and Pitla. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Amlan Balabantaray, University of Nebraska-Lincoln, Lincoln, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.