Review of Current Robotic Approaches for Precision Weed Management.

Area of Science:

• Agricultural Robotics
• Computer Vision in Agriculture
• Precision Weed Management

Background:

• Traditional weed management relies heavily on manual labor and chemical herbicides.
• Increasing environmental concerns and labor shortages necessitate innovative solutions.
• Precision agriculture aims to optimize resource use and minimize environmental impact.

Purpose of the Study:

• To review current robotic approaches in precision weed management.
• To analyze applications and technical challenges in the last five years.
• To identify key areas for future research and development.

Main Methods:

• Literature review of recent (past 5 years) studies on robotic weed management.
• Analysis of computer vision, machine learning, and deep learning applications.
• Assessment of perception, decision-making, and control systems in autonomous weeders.

Main Results:

• Computer vision combined with machine learning and deep learning significantly advances weed detection and mechanical weeding.
• Autonomous weeding robots integrate perception, decision-making, and control for efficient operation.
• Current systems face technical limitations hindering widespread adoption.

Conclusions:

• Robotic weed management offers a sustainable alternative to chemical herbicides, reducing environmental pollution.
• Key research directions include improving technical capabilities and addressing system limitations.
• Widespread utilization of autonomous weeding robots is expected to increase with technological advancements.