Lecturer, Department of Electronics and Communication Engineering, Government Residential Polytechnic for Women’s, Shimoga, Karnataka, India.
Research Article
World Journal of Advanced Research and Reviews, 2021, 12(02), 692-700
Received on 02 November 2021; revised on 15 November 2021; accepted on 21 November 2021
Weeds significantly impact agricultural productivity by competing with crops for essential resources such as nutrients, water, and sunlight, leading to reduced yields and increased farming costs. Traditional weed removal methods, including manual labor and chemical herbicides, present several challenges, such as high labor costs, environmental degradation, and potential health hazards. To address these issues, this paper explores an autonomous weed removal system based on embedded system-driven robotics, designed to enhance precision and efficiency in weed management. The proposed system integrates advanced real-time image processing techniques with machine learning algorithms to accurately distinguish between crops and weeds. Once identified, the system employs robotic actuators for targeted weed elimination, minimizing collateral damage to crops. The embedded system architecture enables adaptive control, optimizing energy consumption while ensuring high operational reliability. Experimental evaluations conducted in controlled agricultural environments demonstrate the system’s effectiveness in reducing weed density, improving weed removal accuracy, and optimizing energy efficiency. The results indicate that the autonomous weed removal system has the potential to revolutionize modern agricultural practices by offering a sustainable, cost-effective, and scalable solution for weed management.
Autonomous Robotics; Embedded Systems; Weed Removal; Precision Agriculture; Machine Learning; Image Processing.
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Guruswamy T. B.. Autonomous weed removal using embedded system-based robotics. World Journal of Advanced Research and Reviews, 2021, 12(2), 692-700. Article DOI: https://doi.org/10.30574/wjarr.2021.12.2.0566