Edge link detector based weed classifier
Proceedings - 2009 International Conference on Digital Image Processing, ICDIP 2009, Page: 255-259
2009
- 7Citations
- 22Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
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. The algorithm is developed to classify images into broad and narrow class for real-time selective herbicide application. The developed algorithm based on Edge Link Detector 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 93 % classification accuracy over 240 sample images (broad, narrow and no or little weeds) with 100 samples from broad weeds, 100 samples from narrow weeds and the remaining 40 from no or little weeds. © 2009 IEEE.
Bibliographic Details
Institute of Electrical and Electronics Engineers (IEEE)
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