Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Artificial Intelligence |
| Untertitel | Concepts, Methodologies, Tools, and Applications |
| Herausgeber (Verlag) | IGI Global |
| Seiten | 1282-1305 |
| Seitenumfang | 24 |
| Band | 2 |
| ISBN (elektronisch) | 9781522517603 |
| ISBN (Print) | 1522517596, 9781522517597 |
| Publikationsstatus | Veröffentlicht - 2017 |
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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Artificial Intelligence: Concepts, Methodologies, Tools, and Applications. Band 2 IGI Global, 2017. S. 1282-1305.
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
PY - 2017
Y1 - 2017
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=85018551392&partnerID=8YFLogxK
U2 - 10.4018/978-1-5225-1759-7.ch052
DO - 10.4018/978-1-5225-1759-7.ch052
M3 - Contribution to book/anthology
AN - SCOPUS:85018551392
SN - 1522517596
SN - 9781522517597
VL - 2
SP - 1282
EP - 1305
BT - Artificial Intelligence
PB - IGI Global
ER -