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All Journal International Journal of Applied and Advanced Multidisciplinary Research (IJAAMR)
Gajbhiye, Prathamesh
Unknown Affiliation
Humanities Civil Engineering, Building, Construction & Architecture Economics, Econometrics & Finance Education Social Sciences

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Machine Learning Based Weed Detection System Gajbhiye, Prathamesh; Meenakshi Thalor
International Journal of Applied and Advanced Multidisciplinary Research Vol. 1 No. 4 (2023): December 2023
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijaamr.v1i4.568

Abstract

This abstract underscores the importance of weed detection in crop cultivation to prevent plant diseases and minimize crop losses. To address these challenges and promote eco-friendly practices, the authors propose a weed detection program employing K-Nearest Neighbors, Random Forest, Decision Tree algorithms, and the YOLOv5 neural network. The abstract also provides a concise overview of existing research in weed identification using machine learning and deep learning. The authors developed a YOLOv5-based weed detection system and evaluated the performance of the algorithm, showing traditional classifiers achieve accuracies of 83.3%, 87.5%, and 80%, while the neural network scores range from 0.82 to 0.92 for each class. The study demonstrates the effectiveness of this approach in classifying low-resolution weed images.
Co-Authors Meenakshi Thalor