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AN ANALYSIS ON MACHINE VISION AND IMAGE PROCESSING TECHNIQUES FOR WEED DETECTION IN AGRICULTURAL CROPS

Author(s)
Rameen Sohail Department of Computer Science, National Textile University Faisalabad,Punjab,Pakistan
Qamar Nawaz Department of Computer Science, National Textile University Faisalabad,Punjab,Pakistan
Isma Hamid Department of Computer Science, National Textile University Faisalabad,Punjab,Pakistan
Syed Mushhad Mustuzhar Gilani University Institute of Information Technology, PMAS-Arid Agriculture University Rawalpindi,Punjab,Pakistan
Imran Mumtaz Department of Computer Science, National Textile University Faisalabad,Punjab,Pakistan
Ahmad Mateen Department of Computer Science, National Textile University Faisalabad,Punjab,Pakistan
Junaid Nawaz Chauhdary Water Management Research Center, University of Agriculture,Faisalabad, Punjab,Pakistan
Abstract
The advancement in the field of precision agriculture has opened doors for site-specific weed management. There is a growing need to control the amount of herbicide sprayed on weeds to reduce economic and environmental losses. In the field of precision agriculture, incorporation of machine learning techniques has enabled the farmers to automate the process of controlling weed using an adequate number of herbicides for different species in-situ. This study aims to explore various parameters of Computer Vision and Machine Learning algorithms and methods used by researchers to develop Artificial Intelligence models to remove weeds from agricultural fields. More than twenty state-of-the-art algorithms have been studied in this paper. We categorized these algorithms into five categories based on different features i.e. visual, shape, spatial, and spectral. At the end of this study, a comprehensive table is presented containing details of algorithms in terms of limitations and accuracy.
Publication Details
Page(s) 187-204
DOI 10.21162/PAKJAS/21.305
Published Journal: Pakistan Journal of Agricultural Sciences, Volume: 58, Issue: 1, Year: 2021

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Keywords
Image Processing, Weed Detection, Machine Vision, Robotic Weed Control
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