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.
Rameen Sohail, Qamar Nawaz, Isma Hamid, Syed Mushhad Mustuzhar Gilani, Imran Mumtaz, Ahmad Mateen, Junaid Nawaz. (2021) A REVIEW ON MACHINE VISION AND IMAGE PROCESSING TECHNIQUES FOR WEED DETECTION IN AGRICULTURAL CROPS, Pakistan Journal of Agricultural Sciences, Volume 58, Issue 1.
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