Weed recognition based on erosion and dilation segmentation algorithm
Farmer needs alternatives for weed control due to the desire to reduce chemicals used in farming. However, conventional mechanical cultivation cannot selectively remove weeds and there are no selective herbicides for some weed situation. Since hand labor is costly, an automated weed control system c...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Published: |
2009
|
| Subjects: | |
| Online Access: | http://eprints.utp.edu.my/159/1/paper.pdf http://www.scopus.com/inward/record.url?eid=2-s2.0-70449587292&partnerID=40&md5=b25f891c99ee3a542267e295cfb70d1c http://eprints.utp.edu.my/159/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Farmer needs alternatives for weed control due to the desire to reduce chemicals used in farming. However, conventional mechanical cultivation cannot selectively remove weeds and there are no selective herbicides for some weed situation. Since hand labor is costly, an automated weed control system could be feasible. A robotic weed control system can also reduce or eliminate the need for chemicals. Many attempts have been made to develop efficient algorithms for recognition and classification. Currently research is going on for developing new machine vision algorithms for automatic recognition and classification of many divers object groups. In this paper an algorithm is developed for automatic spray control system. The algorithm is based on erosion followed by dilation segmentation algorithm. This algorithm can detect weeds and also classify it. Currently the algorithm is tested on two types of weeds i.e. broad and narrow. The developed algorithm has been tested on these two types of weeds in the lab, which gives a very reliable performance. The algorithm is applied on 240 images stored in a database in the lab, of which 100 images were taken from broad leaf weeds and 100 were taken from narrow leaf weeds, and the remaining 40 were taken from no or little weeds. The result showed over 89% results © 2009 IEEE.
|
|---|