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Enabling Precision Agriculture Through Embedded Sensing With Artificial Intelligence

Shadrin, Dmitrii and Menshchikov, Alexander and Somov, Andrey and Bornemann, Gerhild and Hauslage, Jens and Fedorov, Maxim (2020) Enabling Precision Agriculture Through Embedded Sensing With Artificial Intelligence. IEEE Transactions on Instrumentation and Measurement, 69 (7), pp. 4103-4113. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TIM.2019.2947125. ISSN 0018-9456.

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Official URL: https://dx.doi.org/10.1109/TIM.2019.2947125

Abstract

Artificial intelligence (AI) has smoothly penetrated in a number of monitoring and control applications, including agriculture. However, research efforts toward low-power sensing devices with fully functional AI on board are still fragmented. In this article, we present an embedded system enriched with AI, ensuring the continuous analysis and in situ prediction of the growth dynamics of plant leaves. The embedded solution is grounded on a low-power embedded sensing system with a graphics processing unit (GPU) and is able to run the neural network-based AI on board. We use a recurrent neural network (RNN) called the long short-term memory network (LSTM) as a core of AI in our system. The proposed approach guarantees the system autonomous operation for 180 days using a standard Li-ion battery. We rely on the state-of-the-art mobile graphical chips for “smart” analysis and control of autonomous devices. This pilot study opens up wide vista for a variety of intelligent monitoring applications, especially in the agriculture domain. In addition, we share with the research community the Tomato Growth data set.

Item URL in elib:https://elib.dlr.de/135740/
Document Type:Article
Title:Enabling Precision Agriculture Through Embedded Sensing With Artificial Intelligence
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Shadrin, DmitriiSkolkovo Institute of Science and Technology, Center for Computational and Data-Intensive Science and Engineering (CDISE), Moscow, Russiahttps://orcid.org/0000-0003-3486-8214UNSPECIFIED
Menshchikov, AlexanderSkolkovo Institute of Science and Technology, Center for Computational and Data-Intensive Science and Engineering (CDISE), Moscow, Russiahttps://orcid.org/0000-0003-2842-4414UNSPECIFIED
Somov, AndreySkolkovo Institute of Science and Technology, Center for Computational and Data-Intensive Science and Engineering (CDISE), Moscow, Russiahttps://orcid.org/0000-0002-4615-3008UNSPECIFIED
Bornemann, Gerhildgerman aerospace center (dlr), institute of aerospace medicine, gravitational biology, cologne, germanyhttps://orcid.org/0000-0001-7498-3423UNSPECIFIED
Hauslage, Jensgerman aerospace center (dlr), institute of aerospace medicine, gravitational biology, cologne, germanyhttps://orcid.org/0000-0003-2184-7000UNSPECIFIED
Fedorov, MaximSkolkovo Institute of Science and Technology, Center for Computational and Data-Intensive Science and Engineering (CDISE), Moscow, Russiahttps://orcid.org/0000-0003-3901-3565UNSPECIFIED
Date:1 July 2020
Journal or Publication Title:IEEE Transactions on Instrumentation and Measurement
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:69
DOI:10.1109/TIM.2019.2947125
Page Range:pp. 4103-4113
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0018-9456
Status:Published
Keywords:Artificial intelligence (AI), Embedded sensing, precision agriculture, Sensing and control, Smart sensing
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Research under Space Conditions
DLR - Research area:Raumfahrt
DLR - Program:R FR - Research under Space Conditions
DLR - Research theme (Project):R - Projekt :envihab (old), R - Vorhaben Biowissenschaftliche Exp.-vorbereitung (old)
Location: Köln-Porz
Institutes and Institutions:Institute of Aerospace Medicine > Gravitational Biology
Deposited By: Duwe, Helmut
Deposited On:12 Aug 2020 09:51
Last Modified:12 Aug 2020 09:51

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