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@ARTICLE{Agostini:842909,
author = {Agostini, Alejandro and Alenyà, Guillem and Fischbach,
Andreas and Scharr, Hanno and Wörgötter, Florentin and
Torras, Carme},
title = {{A} cognitive architecture for automatic gardening},
journal = {Computers and electronics in agriculture},
volume = {138},
issn = {0168-1699},
address = {Amsterdam [u.a.]},
publisher = {Elsevier Science},
reportid = {FZJ-2018-01082},
pages = {69 - 79},
year = {2017},
abstract = {In large industrial greenhouses, plants are usually treated
following well established protocols for watering,
nutrients, and shading/light. While this is practical for
the automation of the process, it does not tap the full
potential for optimal plant treatment. To more efficiently
grow plants, specific treatments according to the plant
individual needs should be applied. Experienced human
gardeners are very good at treating plants individually.
Unfortunately, hiring a crew of gardeners to carry out this
task in large greenhouses is not cost effective. In this
work we present a cognitive system that integrates
artificial intelligence (AI) techniques for decision-making
with robotics techniques for sensing and acting to
autonomously treat plants using a real-robot platform.
Artificial intelligence techniques are used to decide the
amount of water and nutrients each plant needs according to
the history of the plant. Robotic techniques for sensing
measure plant attributes (e.g. leaves) from visual
information using 3D model representations. These attributes
are used by the AI system to make decisions about the
treatment to apply. Acting techniques execute robot
movements to supply the plants with the specified amount of
water and nutrients.},
cin = {IBG-2},
ddc = {620},
cid = {I:(DE-Juel1)IBG-2-20101118},
pnm = {582 - Plant Science (POF3-582)},
pid = {G:(DE-HGF)POF3-582},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000402360200008},
doi = {10.1016/j.compag.2017.04.015},
url = {https://juser.fz-juelich.de/record/842909},
}