Real-Time Specific Weed Recognition System Using Histogram Analysis
Information on weed distribution within the field is
necessary to implement spatially variable herbicide application.
Since hand labor is costly, an automated weed control system could be
feasible. This paper deals with the development of an algorithm for
real time specific weed recognition system based on Histogram
Analysis of an image that is used for the weed classification. This
algorithm is specifically developed to classify images into broad and
narrow class for real-time selective herbicide application. The
developed system has been tested on weeds in the lab, which have
shown that the system to be very effectiveness in weed identification.
Further the results show a very reliable performance on images of
weeds taken under varying field conditions. The analysis of the results
shows over 95 percent classification accuracy over 140 sample images
(broad and narrow) with 70 samples from each category of weeds.
[1] Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing. 2nd ed.
Delhi: Pearson Education, Inc, 2003, ch 3.
[2] J. Bernd, Digital image processing concepts, algorithms and scientific
applications. Berline: Springer-Verlage,1991, ch 7.
[3] D. E. Guyer, G. E. Miles, M. M. Shreiber, O. R. Mitchell, and V. C.
Vanderbilt, "Machine vision and image processing for plant
identification," Transactions of the ASAE, vol. 29, no.6, pp. 1500-1507,
1986.
[4] J. E. Hanks, "Smart sprayer selects weeds for elimination," Agricultural
Research, vol. 44, no 4, pp. 15, 1996.
[5] R. M. Haralick, K. Shanmugam, and I. Dinstein, " Textural features for
image classification," IEEE Transactions on Systems, Man, and
Cybernetics, SMC, vol. 3, no. 6, pp, 610-621, Nov. 1973.
[6] 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.
[7] L. F. Tian, and D. C. Slaughter, "Environmentally adaptive segmentation
algorithm for outdoor image segmentation," Computers and Electronics
in Agriculture, vol. 21, no. 3, pp. 153-168, 1998.
[8] 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.
[9] D. M. Woebbecke, G. E. Meyer, K. Von Bargen, and D. A. Mortensen,
"Shape features for identifying weeds using image analysis,"
Transactions of the ASAE, vol. 38, no.1, pp. 271-281, 1995.
[1] Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing. 2nd ed.
Delhi: Pearson Education, Inc, 2003, ch 3.
[2] J. Bernd, Digital image processing concepts, algorithms and scientific
applications. Berline: Springer-Verlage,1991, ch 7.
[3] D. E. Guyer, G. E. Miles, M. M. Shreiber, O. R. Mitchell, and V. C.
Vanderbilt, "Machine vision and image processing for plant
identification," Transactions of the ASAE, vol. 29, no.6, pp. 1500-1507,
1986.
[4] J. E. Hanks, "Smart sprayer selects weeds for elimination," Agricultural
Research, vol. 44, no 4, pp. 15, 1996.
[5] R. M. Haralick, K. Shanmugam, and I. Dinstein, " Textural features for
image classification," IEEE Transactions on Systems, Man, and
Cybernetics, SMC, vol. 3, no. 6, pp, 610-621, Nov. 1973.
[6] 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.
[7] L. F. Tian, and D. C. Slaughter, "Environmentally adaptive segmentation
algorithm for outdoor image segmentation," Computers and Electronics
in Agriculture, vol. 21, no. 3, pp. 153-168, 1998.
[8] 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.
[9] D. M. Woebbecke, G. E. Meyer, K. Von Bargen, and D. A. Mortensen,
"Shape features for identifying weeds using image analysis,"
Transactions of the ASAE, vol. 38, no.1, pp. 271-281, 1995.
@article{"International Journal of Biological, Life and Agricultural Sciences:59416", author = "Irshad Ahmad and Abdul Muhamin Naeem and Muhammad Islam", title = "Real-Time Specific Weed Recognition System Using Histogram Analysis", abstract = "Information on weed distribution within the field is
necessary to implement spatially variable herbicide application.
Since hand labor is costly, an automated weed control system could be
feasible. This paper deals with the development of an algorithm for
real time specific weed recognition system based on Histogram
Analysis of an image that is used for the weed classification. This
algorithm is specifically developed to classify images into broad and
narrow class for real-time selective herbicide application. The
developed system has been tested on weeds in the lab, which have
shown that the system to be very effectiveness in weed identification.
Further the results show a very reliable performance on images of
weeds taken under varying field conditions. The analysis of the results
shows over 95 percent classification accuracy over 140 sample images
(broad and narrow) with 70 samples from each category of weeds.", keywords = "Image Processing, real-time recognition, Weeddetection.", volume = "2", number = "4", pages = "104-4", }