Volume 13, Issue 4 · IJARST · ISSN 2319-1783 / 2320-1126

Weed Remover Using Machine Learning

Weed Remover Using Machine Learning is an open-access, peer-reviewed research paper by Hema Swathi S1, Swetha B2, Rohini N3, Subalakshmi V4, published in Volume 13, Issue 4 of the International Journal of Advanced Research in Science and Technology (IJARST), a UGC-approved journal (Print ISSN 2319-1783, Online ISSN 2320-1126).

Author

Hema Swathi S1, Swetha B2, Rohini N3, Subalakshmi V4

Abstract

Weed Remover using Machine Learning is an innovative solution using machine learning techniques to address the persistent challenge of weed management in agriculture. The system integrates advanced machine learning algorithms with robotics to accurately find and cut weeds in real-time. A convolutional neural network (CNN) is trained on a diverse dataset comprising images of various crops and weed species, allowing the model to develop a robust understanding of visual cues associated with both. This trained model is deployed on a robotic platform equipped with cameras and actuators for real-time decision-making and precise weed removal

DOI

https://doi.org/10.62226/ijarst20241339

PAGES : 1308-1312 | 6 VIEWS | 3 DOWNLOADS

How do you cite this paper?

Hema Swathi S1, Swetha B2, Rohini N3, Subalakshmi V4 — “Weed Remover Using Machine Learning.” International Journal of Advanced Research in Science and Technology (IJARST), Volume 13, Issue 4. DOI: https://doi.org/10.62226/ijarst20241339.

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