Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Computer Vision and Pattern Recognition in Environmental Informatics |
| Seiten | 248-272 |
| Seitenumfang | 25 |
| ISBN (elektronisch) | 9781466694361 |
| Publikationsstatus | Veröffentlicht - 19 Okt. 2015 |
Abstract
Small size agricultural robots which are capable of sensing and manipulating the field environment are a promising approach towards more ecological, sustainable and human-friendly agriculture. This chapter proposes a machine vision approach for plant classification in the field and discusses its possible application in the context of robot based precision agriculture. The challenges of machine vision in the field are discussed at the example of plant classification for weed control. Automatic crop/weed discrimination enables new weed control strategies where single weed plants are treated individually. System development and evaluation are done using a dataset of images captured in a commercial organic carrot farm with the autonomous field robot Bonirob under field conditions. Results indicate plant classification performance with 93% average accuracy.
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Computer Vision and Pattern Recognition in Environmental Informatics. 2015. S. 248-272.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung › Peer-Review
}
TY - CHAP
T1 - Plant classification for field robots
T2 - A machine vision approach
AU - Haug, Sebastian
AU - Ostermann, Jörn
N1 - Funding Information: The project RemoteFarming.1 was partially funded by the German Federal Ministry of Food, Agriculture and Consumer Protection (BMELV).
PY - 2015/10/19
Y1 - 2015/10/19
N2 - Small size agricultural robots which are capable of sensing and manipulating the field environment are a promising approach towards more ecological, sustainable and human-friendly agriculture. This chapter proposes a machine vision approach for plant classification in the field and discusses its possible application in the context of robot based precision agriculture. The challenges of machine vision in the field are discussed at the example of plant classification for weed control. Automatic crop/weed discrimination enables new weed control strategies where single weed plants are treated individually. System development and evaluation are done using a dataset of images captured in a commercial organic carrot farm with the autonomous field robot Bonirob under field conditions. Results indicate plant classification performance with 93% average accuracy.
AB - Small size agricultural robots which are capable of sensing and manipulating the field environment are a promising approach towards more ecological, sustainable and human-friendly agriculture. This chapter proposes a machine vision approach for plant classification in the field and discusses its possible application in the context of robot based precision agriculture. The challenges of machine vision in the field are discussed at the example of plant classification for weed control. Automatic crop/weed discrimination enables new weed control strategies where single weed plants are treated individually. System development and evaluation are done using a dataset of images captured in a commercial organic carrot farm with the autonomous field robot Bonirob under field conditions. Results indicate plant classification performance with 93% average accuracy.
UR - http://www.scopus.com/inward/record.url?scp=84982899808&partnerID=8YFLogxK
U2 - 10.4018/978-1-4666-9435-4.ch012
DO - 10.4018/978-1-4666-9435-4.ch012
M3 - Contribution to book/anthology
AN - SCOPUS:84982899808
SN - 1466694351
SN - 9781466694354
SP - 248
EP - 272
BT - Computer Vision and Pattern Recognition in Environmental Informatics
ER -