Targeted weed management of Palmer amaranth using robotics and deep learning (YOLOv7)

Authors

Amlan Balabantaray, University of Nebraska-LincolnFollow
Shaswati Behera, University of Nebraska-LincolnFollow
CheeTown Liew, University of Nebraska-Lincoln
Nipuna Chamara, University of Nebraska-LincolnFollow
Mandeep Singh, University of Nebraska-LincolnFollow
Amit J. Jhala, University of Nebraska-LincolnFollow
Santosh Pitla, University of Nebraska-LincolnFollow

Document Type

Article

Date of this Version

9-26-2024

Citation

Balabantaray A, Behera S, Liew C, Chamara N, Singh M, Jhala AJ and Pitla S (2024) Targeted weed management of Palmer amaranth using robotics and deep learning (YOLOv7). Front. Robot. AI 11:1441371. doi: 10.3389/frobt.2024.1441371

Comments

Open access

Abstract

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. The Weeding robot successfully identified Palmer amaranth across diverse growth stages in controlled outdoor conditions. This study demonstrates the potential of AI-enabled robotic systems for targeted weed management, offering a more precise and sustainable alternative to traditional herbicide application methods.