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A cognitive architecture for automatic gardening

AuthorsAgostini, Alejandro ; Alenyà, Guillem ; Fischbach, Andreas; Scharr, Hanno; Wörgötter, Florentin; Torras, Carme
KeywordsCognitive architecture
Learning planning operators
Human-robot interaction
Automatic gardening
Optimized plant treatments
Issue Date2017
CitationComputers and Electronics in Agriculture 138: 69-79 (2017)
AbstractIn 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.
Publisher version (URL)https://doi.org/10.1016/j.compag.2017.04.015
Identifiersdoi: 10.1016/j.compag.2017.04.015
issn: 0168-1699
e-issn: 1872-7107
Appears in Collections:(IRII) Artículos
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