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Agriculture faces significant challenges due to the growth
of weeds, which compete with crops for essential resources such as
nutrients, water, and sunlight, thereby reducing overall yield. This
project presents a smart selective weeding machine that utilizes image
classification techniques to distinguish between crops and weeds in real
time. A camera captures live images, which are processed using a
trained machine learning model implemented with TensorFlow Lite.
Based on the classification results, the system controls a robot via
Arduino: the robot moves forward when crops are detected and stops
when weeds are identified. For demonstration reliability, a timed
control mechanism is also incorporated. This project demonstrates the
effective integration of computer vision, machine learning, and
embedded systems to provide a low-cost and efficient solution for
precision agriculture.
"Smart Selective Weeding Machine Using Image Processing ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.b791-b795, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605195.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator