Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2014 IEEE Winter Conference on Applications of Computer Vision |
| Untertitel | WACV 2014 |
| Seiten | 1142-1149 |
| Seitenumfang | 8 |
| Publikationsstatus | Veröffentlicht - Juni 2014 |
| Veranstaltung | 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 - Steamboat Springs, CO, USA / Vereinigte Staaten Dauer: 24 März 2014 → 26 März 2014 |
Abstract
This paper proposes a machine vision approach for plant classification without segmentation and its application in agriculture. Our system can discriminate crop and weed plants growing in commercial fields where crop and weed grow close together and handles overlap between plants. Automated crop / weed discrimination enables weed control strategies with specific treatment of weeds to save cost and mitigate environmental impact.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Angewandte Informatik
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
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2014 IEEE Winter Conference on Applications of Computer Vision: WACV 2014. 2014. S. 1142-1149 6835733.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Plant Classification System for Crop / Weed Discriminationwithout Segmentation
AU - Haug, Sebastian
AU - Michaels, Andreas
AU - Biber, Peter
AU - Ostermann, Jorn
N1 - Funding Information: The project RemoteFarming.1 is partially funded by the German Federal Ministry of Food, Agriculture and Con- sumer Protection (BMELV).
PY - 2014/6
Y1 - 2014/6
N2 - This paper proposes a machine vision approach for plant classification without segmentation and its application in agriculture. Our system can discriminate crop and weed plants growing in commercial fields where crop and weed grow close together and handles overlap between plants. Automated crop / weed discrimination enables weed control strategies with specific treatment of weeds to save cost and mitigate environmental impact.
AB - This paper proposes a machine vision approach for plant classification without segmentation and its application in agriculture. Our system can discriminate crop and weed plants growing in commercial fields where crop and weed grow close together and handles overlap between plants. Automated crop / weed discrimination enables weed control strategies with specific treatment of weeds to save cost and mitigate environmental impact.
UR - http://www.scopus.com/inward/record.url?scp=84904705432&partnerID=8YFLogxK
U2 - 10.1109/wacv.2014.6835733
DO - 10.1109/wacv.2014.6835733
M3 - Conference contribution
AN - SCOPUS:84904705432
SN - 9781479949854
SP - 1142
EP - 1149
BT - 2014 IEEE Winter Conference on Applications of Computer Vision
T2 - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
Y2 - 24 March 2014 through 26 March 2014
ER -