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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM41603

AgroWeedGuard : Smart Weed Detection for a healthier Harvest using CNN and Deep Learning

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM41603

Volume:

9 |

2 |

Feb 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM41603

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

TSK

T.Bhavya Sai Keerthi1

TSS

T.Bhavana Sai Sneha2

KSB

K.Yuva Surya Bhagavan3

M

M.Lajith4

R

R.Rajender5

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Abstract

Weed infestation negatively impacts crop yields and increases farming costs. AgroWeedGuard is a smart weed detection system that utilizes Convolutional Neural Networks (CNN) and Deep Learning to identify and classify weeds in agricultural fields. The model processes real-time images to distinguish weeds from crops with high accuracy. By automating weed detection, it reduces reliance on manual labor and minimizes excessive herbicide use, promoting sustainable farming. Experimental results validate the system’s efficiency in diverse field conditions, demonstrating its potential to enhance agricultural productivity and ensure healthier harvests.

KEYWORDS

Image Processing Machine Learning Object Detection Deep Learning Convolutional Neural Networks and Real-Time Analysis YOLO V5.

Cite This Article

T.Bhavya Sai Keerthi1, T.Bhavana Sai Sneha2, K.Yuva Surya Bhagavan3, M.Lajith4, R.Rajender5, "AgroWeedGuard : Smart Weed Detection for a healthier Harvest using CNN and Deep Learning," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 2, Feb 2025. https://doi.org/10.55041/IJSREM41603

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Publication Date

2/17/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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0

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3

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5

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The authors declare no conflict of interest related to this research work.

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