Abstract
In this paper we address the problem of recognising the Broad-leaved dock (Rumex obtusifolius L.) in grasslands from high-resolution 2D images. We discuss and present the determining factors for developing and implementing weed visual recognition algorithms using deep learning. This analysis, leads to the formulation of the proposed algorithm. Our implementation exploits Transfer Learning techniques for deep learning-based feature extraction, in combination with a classifier for weed recognition. A prototype robotic platform has been used to make available an image dataset from a dairy farm containing broad-leaved docks. The evaluation of the proposed algorithm on this dataset shows that it outperforms competing weed/plant recognition methods in recognition accuracy, while producing low false-positive rates under real-world operation conditions.
| Original language | English |
|---|---|
| Article number | 104973 |
| Journal | Computers and Electronics in Agriculture |
| Volume | 165 |
| Number of pages | 11 |
| ISSN | 0168-1699 |
| DOIs | |
| Publication status | Published - 1 Oct 2019 |
Keywords
- Agricultural robotics
- Deep learning visual recognition
- Precision farming
- Weed recognition
Cite this
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Deep learning-based visual recognition of rumex for robotic precision farming. / Kounalakis, Tsampikos; Triantafyllidis, Georgios A.; Nalpantidis, Lazaros.
In: Computers and Electronics in Agriculture, Vol. 165, 104973, 01.10.2019.Research output: Contribution to journal › Journal article › Research › peer-review
TY - JOUR
T1 - Deep learning-based visual recognition of rumex for robotic precision farming
AU - Kounalakis, Tsampikos
AU - Triantafyllidis, Georgios A.
AU - Nalpantidis, Lazaros
PY - 2019/10/1
Y1 - 2019/10/1
N2 - In this paper we address the problem of recognising the Broad-leaved dock (Rumex obtusifolius L.) in grasslands from high-resolution 2D images. We discuss and present the determining factors for developing and implementing weed visual recognition algorithms using deep learning. This analysis, leads to the formulation of the proposed algorithm. Our implementation exploits Transfer Learning techniques for deep learning-based feature extraction, in combination with a classifier for weed recognition. A prototype robotic platform has been used to make available an image dataset from a dairy farm containing broad-leaved docks. The evaluation of the proposed algorithm on this dataset shows that it outperforms competing weed/plant recognition methods in recognition accuracy, while producing low false-positive rates under real-world operation conditions.
AB - In this paper we address the problem of recognising the Broad-leaved dock (Rumex obtusifolius L.) in grasslands from high-resolution 2D images. We discuss and present the determining factors for developing and implementing weed visual recognition algorithms using deep learning. This analysis, leads to the formulation of the proposed algorithm. Our implementation exploits Transfer Learning techniques for deep learning-based feature extraction, in combination with a classifier for weed recognition. A prototype robotic platform has been used to make available an image dataset from a dairy farm containing broad-leaved docks. The evaluation of the proposed algorithm on this dataset shows that it outperforms competing weed/plant recognition methods in recognition accuracy, while producing low false-positive rates under real-world operation conditions.
KW - Agricultural robotics
KW - Deep learning visual recognition
KW - Precision farming
KW - Weed recognition
U2 - 10.1016/j.compag.2019.104973
DO - 10.1016/j.compag.2019.104973
M3 - Journal article
VL - 165
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
SN - 0168-1699
M1 - 104973
ER -