A Review on AI Assisted Laser-Based Weed Management Frameworks for Precision and Sustainable Agriculture

Vikas Madhavrao Somvanshi^1*, Ashvini Sunil Kolate², Jayesh Nana Patil³ and Prayag Satish Patil⁴

¹Department of Computer  Engineering, SSVPS B.S DeorePolytechnic, Dhule, India.

²Department of Computer Science, PO Nahata College, Bhusawal, India.

³Department of Computer Engineering, Patel College of Science and Technology, Indore, India.

⁴Department of Design and Automation , Vellore Institute of Technology, Vellore, India.

Corresponding Author Email: vikassomvanshi13@gmail.com

Abstract:

Laser weeding is emerging as a promising precision agriculturetechnology for sustainable and chemical-free weed management. Weeds reduce global cropproductivity by nearly 20–40% annually.Due to excessive use of herbicides andmechanical tillage causes environmental contamination, soildegradation, and increased production costs.Recent advancements in AI, machine vision,robotics, and laser technologies have enabled the development of autonomous laser weedingsystems.These systems capable of detection and destroy weeds with high spatial precision without crop injury and soil disturbance. Currently available experimental and commercial platforms havereported weed control efficiencies ranging from approximately 85–99%. Adding to it few autonomoussystems managed over 200,000 weeds per hour under optimized field conditions.Earlier review studies mainly focused on isolated aspects such as laser hardware,robotic platforms, or weed detection algorithms while this review provides a comprehensive andintegrated comparative analysis of AI-assisted laser weed management frameworks. It includes relevant methods ofcombining laser technologies, sensing systems, deep learning-based weed detection models,robotic platforms, operational constraints, energy efficiency, and sustainability considerations. The review critically compares CO2, diode,fiber, and blue diode laser systems with AI models including CNN, YOLO, Mask R-CNN,and EfficientDet for real-time weed detection and targeting applications.In addition, this study identifies major technological and practical limitations such as highcapital cost, field variability, real-time processing constraints, energy consumption, scalabilitychallenges, and limited AI generalization capability.Future research opportunities involving edge AI, multi-sensor fusion, autonomous robotic fleets,explainable AI, and energy-efficient laser systems are also highlighted to support weed management. Overall, this review contributes atechnological framework and future roadmap for the development of AI-assistedlaser-based weed management systems in precision and sustainable agriculture.

Keywords:

AI-assisted detection; Autonomous robotics; Blue diode laser; Integrated Weed Management;Laser weeding; Precision agriculture; Sustainable weed control

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