Design and Development of an Autonomous Machine Vision-Based Weed Detection and Removal Robot for Agriculture 4.0 Applications
Keywords:
Autonomous Robot, Weed Detection, Machine Vision, Deep Learning, Convolutional Neural Network (CNN), Precision Agriculture, Agricultural Robotics, Smart Farming, Agriculture 4.0Abstract
Weed growth significantly reduces agricultural productivity by competing with crops for essential resources such as nutrients, water, and sunlight. Conventional weed control methods, including manual labor and chemical herbicides, are often inefficient, labor-intensive, and environmentally unsustainable. This paper presents the design and development of an autonomous machine vision-based weed detection and removal system for Agriculture 4.0 applications. The proposed system integrates a vision module for real-time image acquisition, a convolutional neural network (CNN) for crop–weed classification, and a robotic actuation mechanism for targeted weed removal. The system is designed to operate under varying field conditions and enables precise intervention without damaging crops. Experimental evaluation indicates that the system achieves classification accuracy in the range of approximately 88–93%, demonstrating reliable performance in real-time scenarios. The proposed approach reduces dependency on chemical herbicides and labor while contributing to sustainable and intelligent farming practices.
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Copyright (c) 2026 Vaishnavi D. Singare, Avinash A. Somatkar (Author)
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