Imran Ahmed and Muhammad Islam and Syed Inayat Ali Shah and Awais Adnan
A RealTime Specific Weed Recognition System Using Statistical Methods
2975 - 2981
2007
1
10
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/9517
https://publications.waset.org/vol/10
World Academy of Science, Engineering and Technology
The identification and classification of weeds are of
major technical and economical importance in the agricultural
industry. To automate these activities, like in shape, color and
texture, weed control system is feasible. The goal of this paper is to
build a realtime, machine vision weed control system that can detect
weed locations. In order to accomplish this objective, a realtime
robotic system is developed to identify and locate outdoor plants
using machine vision technology and pattern recognition. The
algorithm is developed to classify images into broad and narrow class
for realtime selective herbicide application. The developed
algorithm has been tested on weeds at various locations, which have
shown that the algorithm to be very effectiveness in weed
identification. Further the results show a very reliable performance
on weeds under varying field conditions. The analysis of the results
shows over 90 percent classification accuracy over 140 sample
images (broad and narrow) with 70 samples from each category of
weeds.
Open Science Index 10, 2007