Commenced in January 2007
Paper Count: 30371
Weed Classification using Histogram Maxima with Threshold for Selective Herbicide Applications
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 Maxima with threshold 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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1327750Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1753
 Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing. 2nd ed. Delhi: Pearson Education, Inc, 2003, ch 3.
 J. Bernd, Digital image processing concepts, algorithms and scientific applications. Berlin: Springer-Verlage, 1991, ch 7.
 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.
 J. E. Hanks, ''Smart sprayer selects weeds for elimination,'' Agricultural Research, vol. 44, no 4, pp. 15, 1996.
 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.
 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.
 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.
 J. S. Weszka, C. R. Dyer, and A. Rosenfeld, ''A comparative study of texture measures for terrain classification,'' IEEE Transactions on Systems, Man, and Cybernetics, SMC, vol. 6, pp. 269-285, 1976.
 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.