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Authors: Massimiliano Proietti 1 ; Federico Bianchi 2 ; 1 ; Andrea Marini 1 ; Lorenzo Menculini 1 ; Loris Francesco Termite 3 ; Alberto Garinei 2 ; 1 ; Lorenzo Biondi 2 ; 1 and Marcello Marconi 2 ; 1

Affiliations: 1 Idea-Re S.r.l., Perugia, Italy ; 2 Department of Sustainability Engineering, Guglielmo Marconi University, Rome, Italy ; 3 K-Digitale S.r.l., Perugia, Italy

Keyword(s): Greenhouse Farming, Deep Learning, Computer Vision, Edge Intelligence, Anomaly Detection, Encoder-Decoder, Smart Local Systems.

Abstract: This paper presents a methodology to control greenhouse operations based on deep learning. The proposed methodology employs Artificial Intelligence algorithms working on edge devices, allowing the detection of anomalies in plants growth and greenhouse control equipment, in view of taking possible corrective actions. Edge Intelligence allows the greenhouse to work independently of the network to which it is connected. It also guarantees privacy to the processed data and contributes to fast and efficient decision-making. In this work, a Long-Short Time Memory Encoder-Decoder architecture is used for greenhouse anomaly detection. The best performance is achieved when using one LSTM layer and 64 LSTM units.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Proietti, M.; Bianchi, F.; Marini, A.; Menculini, L.; Termite, L.; Garinei, A.; Biondi, L. and Marconi, M. (2021). Edge Intelligence with Deep Learning in Greenhouse Management. In Proceedings of the 10th International Conference on Smart Cities and Green ICT Systems - SMARTGREENS; ISBN 978-989-758-512-8; ISSN 2184-4968, SciTePress, pages 180-187. DOI: 10.5220/0010451701800187

@conference{smartgreens21,
author={Massimiliano Proietti. and Federico Bianchi. and Andrea Marini. and Lorenzo Menculini. and Loris Francesco Termite. and Alberto Garinei. and Lorenzo Biondi. and Marcello Marconi.},
title={Edge Intelligence with Deep Learning in Greenhouse Management},
booktitle={Proceedings of the 10th International Conference on Smart Cities and Green ICT Systems - SMARTGREENS},
year={2021},
pages={180-187},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010451701800187},
isbn={978-989-758-512-8},
issn={2184-4968},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Smart Cities and Green ICT Systems - SMARTGREENS
TI - Edge Intelligence with Deep Learning in Greenhouse Management
SN - 978-989-758-512-8
IS - 2184-4968
AU - Proietti, M.
AU - Bianchi, F.
AU - Marini, A.
AU - Menculini, L.
AU - Termite, L.
AU - Garinei, A.
AU - Biondi, L.
AU - Marconi, M.
PY - 2021
SP - 180
EP - 187
DO - 10.5220/0010451701800187
PB - SciTePress