A Real-Time Specific Weed Recognition System Using Statistical Methods
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 real-time, machine vision weed control system that can detect
weed locations. In order to accomplish this objective, a real-time
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 real-time 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.
[1] ISBN 0-7167-1031-5 Janick, Jules. Horticultural Science. San Francisco:
W.H. Freeman, 1979. Page 308.
[2] B. L. Steward And L. F. Tian, "Real-Time Weed Detection In Outdoor
Field Conditions," In Proc. Spie Vol. 3543, Precision Agriculture And
Biological Quality, Boston, Ma, Jan. 1999, Pp. 266-278.
[3] J. E. Hanks, "Smart Sprayer Selects Weeds for Elimination,"
Agricultural Research, Vol. 44, No 4, Pp. 15, 1996.
[4] J. S. Weszka, C. R. Dyer, And A. Rosenfeld, "A Comparative Study
Of Texture Measures For Terrain Classification," IEEE Transactions on
Systems, Man, And Ccybernetics , Smc, Vol. 6, Pp. 269-285, 1976.
[5] Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing. 2nd
Ed. Delhi: Pearson Education, Inc, 2003, Page 617,618.
[6] Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing. 2nd
Ed. Delhi: Pearson Education, Inc, 2003, Page 119,161,167,172.
[7] Rulph Chasseing, Digital Signal Processing With C and the Tms320c30,
Mcgraw-Hill, Inc.
[8] M.A.Sid-Ahmed, Image Processing Theroyalgorithms & Arghitectures,
Mcgraw-Hill, Inc.
[9] Graig A. Lindley. Practal Image Processing In C. Acquisition.
Manipulation. Storage.
[10] Paul Davies. The Indispensable Guide To C, First Printed 1995,
Reprinted 1996.
[11] Arun D. Kulkarni, Computer Vision And Fuzzy-Neural Systems,
Prentice Hall Ptr.
[12] Beck, J. A. Sutter And R. Ivry. 1987. Spatial Frequency Channels and
Perceptual Grouping In Texture Segregation. Computer Vision,
Graphics, And Image Processing.
[1] ISBN 0-7167-1031-5 Janick, Jules. Horticultural Science. San Francisco:
W.H. Freeman, 1979. Page 308.
[2] B. L. Steward And L. F. Tian, "Real-Time Weed Detection In Outdoor
Field Conditions," In Proc. Spie Vol. 3543, Precision Agriculture And
Biological Quality, Boston, Ma, Jan. 1999, Pp. 266-278.
[3] J. E. Hanks, "Smart Sprayer Selects Weeds for Elimination,"
Agricultural Research, Vol. 44, No 4, Pp. 15, 1996.
[4] J. S. Weszka, C. R. Dyer, And A. Rosenfeld, "A Comparative Study
Of Texture Measures For Terrain Classification," IEEE Transactions on
Systems, Man, And Ccybernetics , Smc, Vol. 6, Pp. 269-285, 1976.
[5] Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing. 2nd
Ed. Delhi: Pearson Education, Inc, 2003, Page 617,618.
[6] Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing. 2nd
Ed. Delhi: Pearson Education, Inc, 2003, Page 119,161,167,172.
[7] Rulph Chasseing, Digital Signal Processing With C and the Tms320c30,
Mcgraw-Hill, Inc.
[8] M.A.Sid-Ahmed, Image Processing Theroyalgorithms & Arghitectures,
Mcgraw-Hill, Inc.
[9] Graig A. Lindley. Practal Image Processing In C. Acquisition.
Manipulation. Storage.
[10] Paul Davies. The Indispensable Guide To C, First Printed 1995,
Reprinted 1996.
[11] Arun D. Kulkarni, Computer Vision And Fuzzy-Neural Systems,
Prentice Hall Ptr.
[12] Beck, J. A. Sutter And R. Ivry. 1987. Spatial Frequency Channels and
Perceptual Grouping In Texture Segregation. Computer Vision,
Graphics, And Image Processing.
@article{"International Journal of Information, Control and Computer Sciences:58586", author = "Imran Ahmed and Muhammad Islam and Syed Inayat Ali Shah and Awais Adnan", title = "A Real-Time Specific Weed Recognition System Using Statistical Methods", abstract = "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 real-time, machine vision weed control system that can detect
weed locations. In order to accomplish this objective, a real-time
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 real-time 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.", keywords = "Weed detection, Image Processing, real-timerecognition, Standard Deviation.", volume = "1", number = "10", pages = "3182-7", }