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A cognitive architecture for automatic gardening

By Alejandro Gabriel Agostini, Guillem Alenyà Ribas, Andreas Fischbach, Hanno Scharr, Florentin Woergoetter and Carme Torras

Abstract

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/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.Peer Reviewe

Topics: Àrees temàtiques de la UPC::Informàtica::Robòtica, Cognitive architecture, Optimized plant treatments, Automatic gardening, Human-robot interaction, Learning planning operators, Classificació INSPEC::Automation::Robots
Year: 2017
DOI identifier: 10.1016/j.compag.2017.04.015
OAI identifier: oai:upcommons.upc.edu:2117/115431
Provided by: UPCommons
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