ARTIFICIAL INTELLIGENCE FOR WEED DETECTION - A TECHNO-EFFICIENT APPROACH

ICTACT Journal on Image and Video Processing ( Volume: 11 , Issue: 2 )

Abstract

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Technology is improving the ways, methods and approaches many old and tedious tasks are being performed such that the thoughts of the in-existence of these present technologies about some decades ago may negatively change one’s mood. The emergence of Artificial Intelligence has significantly improved almost every aspect of human life, considering the enormous roles it has begun to play in agriculture, researchers and scientists are seeking better ways of improving and ensuring optimal efficiency of the developed systems. This paper gives an insight into the application of Artificial Intelligence (AI) as the best techno-efficient approach for weed detection. A collection of fifty (50) various research works reported by researchers on weed detection in the years ranging from 2012 to 2020 were sampled and examined. A streamlined classification and categorization was performed based on their degree of relevancy to the study. The adopted methods, nature of inputs, processing methods and result obtained from the various study were considered. The roles played by the application of intelligent systems were highlighted with a view of broadening reasoning and channelling future researches towards developing better and more techno-efficient intelligent systems to aid in agricultural related activities. Research findings indicate that technological improvements towards the introduction and usage of Artificial Intelligent (AI) systems will result in a more techno-efficient method for weed detection in agriculture.

Authors

Femi Temitope Johnson, Joel Akerele
Federal University of Agriculture, Nigeria

Keywords

Artificial Intelligence, Agriculture, Robotics, Machine Vision, Thresholding, Weeds Detection, Machine Learning

Published By
ICTACT
Published In
ICTACT Journal on Image and Video Processing
( Volume: 11 , Issue: 2 )
Date of Publication
November 2020
Pages
2297-2305

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