Summary
This paper presents a simulation-based framework for developing an agricultural robotic system capable of selective weed detection and control. The system combines convolutional neural networks with a 2DOF robotic manipulator and is evaluated in a virtual environment to assess perception accuracy and intervention feasibility. The work demonstrates the potential of simulation-based design whilst identifying key challenges for transitioning to real-world deployment.
Regional applicability
The study is technology-focused rather than field-tested, limiting direct applicability to United Kingdom farming practice. However, the simulation framework and vision-based approach to selective herbicide application could support development of precision weed control systems adapted to UK crop types and field conditions, subject to real-world validation and agronomic efficacy trials.
Key measures
Detection accuracy, localisation error, intervention success rate, camera calibration performance, kinematic model performance
Outcomes reported
The study evaluated a robotic system integrating convolutional neural networks and kinematic modelling for weed detection, classification, and selective intervention in a simulated environment. Key metrics included detection accuracy, localisation error, and intervention success rate under simulated field conditions.
Topic tags
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