A Real-Time Specific Weed Recognition System by Measuring Weeds Density through Mask Operation
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 which is based on Measuring Weeds Density through Mask operation 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 95 % classification accuracy over 170 sample images (broad and narrow) with 70 samples from each category of weeds.
KeywordsWeed detection Image Processing real-time recognition weed density mask
Unable to display preview. Download preview PDF.
- ISBN 0-7167-1031-5 Janick, Jules.Horticultural Science. San Francisco: W.H. Freeman, 1979. Page 308.Google Scholar
- 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.Google Scholar
- J. E. Hanks, “Smart sprayer selects weeds for elimination,”Agricultural Research, vol. 44, no 4, pp. 15, 1996.Google Scholar
- Rafael C. Gonzalez, Richard E. Woods,Digital Image Processing. 2nd ed. Delhi: Pearson Education, Inc, 2003, page 617,618Google Scholar
- Rafael C. Gonzalez, Richard E. Woods,Digital Image Processing. 2nd ed. Delhi: Pearson Education, Inc, 2003, page 119,161,167,172Google Scholar
- Rulph chasseing, Digital Signal Processing with C and the TMS320C30, McGraw-Hill, Inc. Google Scholar
- M.A.SID-AHMED, Image Processing THEROYALGORITHMS&Arghitectures, McGraw-Hill, Inc. Google Scholar
- Graig A. Lindley. PRACTAL IMAGE PROCESSING IN C. Acquisition. Manipulation. Storage Google Scholar
- Paul Davies. The Indispensable Guide to C, First printed 1995, Reprinted 1996. Google Scholar
- ARUN D. KULKARNI, Computer Vision and Fuzzy-Neural Systems, Prentice Hall PTR. Google Scholar
- Beck, J. A. Sutter and R. Ivry. 1987. Spatial frequency channels and perceptual grouping in texture segregation. Computer Vision, Graphics, and Image Processing Google Scholar