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Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning

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Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning

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Guillén-Navarro, MA.; Llanes, A.; Imbernón, B.; Martínez-España, R.; Bueno-Crespo, A.; Cano, J.; Cecilia-Canales, JM. (2021). Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning. The Journal of Supercomputing. 77:818-840. https://doi.org/10.1007/s11227-020-03288-w

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/196324

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Title: Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning
Author: Guillén-Navarro, Miguel A. Llanes, Antonio Imbernón, Baldomero Martínez-España, Raquel Bueno-Crespo, Andrés Cano, Juan-Carlos Cecilia-Canales, José María
UPV Unit: Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica
Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors
Issued date:
Embargo end date: 1999-01-01
Abstract:
[EN] The Internet of Things (IoT) is driving the digital revolution. AlSome palliative measures aremost all economic sectors are becoming "Smart" thanks to the analysis of data generated by IoT. This analysis is carried ...[+]
Subjects: Edge computing , LSTM , Deep learning , Precision Agriculture
Copyrigths: Reserva de todos los derechos
Source:
The Journal of Supercomputing. (issn: 0920-8542 )
DOI: 10.1007/s11227-020-03288-w
Publisher:
Springer-Verlag
Publisher version: https://doi.org/10.1007/s11227-020-03288-w
Project ID:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-096384-B-I00/ES/SOLUCIONES PARA UNA GESTION EFICIENTE DEL TRAFICO VEHICULAR BASADAS EN SISTEMAS Y SERVICIOS EN RED/
info:eu-repo/grantAgreement/AGENCIA ESTATAL DE INVESTIGACION//RTC-2017-6389-5-AR//PLANIFICACIÓN Y GESTIÓN DE RECURSOS HÍDRICOS A PARTIR DE ANÁLISIS DE DATOS DE IOT/
info:eu-repo/grantAgreement/f SéNeCa//20813%2FPI%2F18/
Thanks:
This work was partially supported by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities ...[+]
Type: Artículo

References

Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, et al. (2016) Tensorflow: a system for large-scale machine learning. In: 12th $$\{$$USENIX$$\}$$ Symposium on Operating Systems Design and Implementation ($$\{$$OSDI$$\}$$ 16), pp 265–283

Aliberti A, Bottaccioli L, Macii E, Di Cataldo S, Acquaviva A, Patti E (2019) A non-linear autoregressive model for indoor air-temperature predictions in smart buildings. Electronics 8(9):979

Bah MD, Dericquebourg E, Hafiane A, Canals R (2018) Deep learning based classification system for identifying weeds using high-resolution UAV imagery. In: Science and Information Conference. Springer, pp 176–187 [+]
Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, et al. (2016) Tensorflow: a system for large-scale machine learning. In: 12th $$\{$$USENIX$$\}$$ Symposium on Operating Systems Design and Implementation ($$\{$$OSDI$$\}$$ 16), pp 265–283

Aliberti A, Bottaccioli L, Macii E, Di Cataldo S, Acquaviva A, Patti E (2019) A non-linear autoregressive model for indoor air-temperature predictions in smart buildings. Electronics 8(9):979

Bah MD, Dericquebourg E, Hafiane A, Canals R (2018) Deep learning based classification system for identifying weeds using high-resolution UAV imagery. In: Science and Information Conference. Springer, pp 176–187

Bonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing. ACM, pp 13–16

Boubin J, Chumley J, Stewart C, Khanal S (2019) Autonomic computing challenges in fully autonomous precision agriculture. In: 2019 IEEE International Conference on Autonomic Computing (ICAC), pp 11–17. IEEE

Cass S (2019) Taking AI to the edge: Google’s TPU now comes in a maker-friendly package. IEEE Spectr 56(5):16–17

Chen X, Shi Q, Yang L, Xu J (2018) Thriftyedge: Resource-efficient edge computing for intelligent IoT applications. IEEE Netw 32(1):61–65

Chetlur S, Woolley C, Vandermersch P, Cohen J, Tran J, Catanzaro B, Shelhamer E (2014) cuDNN: efficient primitives for deep learning. arXiv preprint arXiv:1410.0759

Farooq MS, Riaz S, Abid A, Abid K, Naeem MA (2019) A survey on the role of IoT in agriculture for the implementation of smart farming. IEEE Access 7:156237–156271

Gondchawar N, Kawitkar R (2016) Iot based smart agriculture. Int J Adv Res Comput Commun Eng 5(6):838–842

Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge

Graves A, Mohamed Ar, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp 6645–6649. IEEE

Grinblat GL, Uzal LC, Larese MG, Granitto PM (2016) Deep learning for plant identification using vein morphological patterns. Comput Electron Agric 127:418–424

Guillén-Navarro MA, Martínez-España R, Llanes A, Bueno-Crespo A, Cecilia JM (2020) A deep learning model to predict lower temperatures in agriculture. J Ambient Intell Smart Environ 12(1):21–34

Guillén-Navarro MA, Martínez-España R, López B, Cecilia JM (2019) A high-performance IoT solution to reduce frost damages in stone fruits. Concurr Comput Pract Exper. https://doi.org/10.1002/cpe.5299

Gulli A, Pal S (2017) Deep learning with Keras. Packt Publishing Ltd, Birmingham

Halawa H, Abdelhafez HA, Boktor A, Ripeanu M (2017) Nvidia Jetson platform characterization. In: EuropEan Conference on Parallel Processing. Springer, pp 92–105

Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

Kamilaris A, Prenafeta-Boldu FX (2018) Deep learning in agriculture: a survey. Comput Electron Agric 147:70–90

Khosravi A, Koury R, Machado L, Pabon J (2018) Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system. Sustain Energy Technol Assessm 25:146–160

Kratzert F, Klotz D, Brenner C, Schulz K, Herrnegger M (2018) Rainfall-Runoff modelling using Long-Short-Term-Memory (LSTM) networks. Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-247

Kussul N, Lavreniuk M, Skakun S, Shelestov A (2017) Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci Remote Sens Lett 14(5):778–782

Kuwata K, Shibasaki R (2015) Estimating crop yields with deep learning and remotely sensed data. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, pp 858–861

Li Y, Zhao K, Chu X, Liu J (2013) Speeding up k-means algorithm by gpus. J Comput Syst Sci 79(2):216–229

Liakos K, Busato P, Moshou D, Pearson S, Bochtis D (2018) Machine learning in agriculture: a review. Sensors 18(8):2674

Mohammadi M, Al-Fuqaha A, Sorour S, Guizani M (2018) Deep learning for iot big data and streaming analytics: A survey. IEEE Commun Surv Tutor 20(4):2923–2960

Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419

Pierpaoli E, Carli G, Pignatti E, Canavari M (2013) Drivers of precision agriculture technologies adoption: a literature review. Proc Technol 8:61–69

Qi S, Wan L, Fu B (2018) Multisource and multiuser water resources allocation based on genetic algorithm. J Supercomput, pp 1–9

Rodriguez SAB, Klein L, Schrott AG, Van Kessel TG (2019) Autonomous mobile platform and variable rate irrigation method for preventing frost damage . US Patent App. 10/219,448

Salman AG, Heryadi Y, Abdurahman E, Suparta W (2018) Single layer & multi-layer long short-term memory (lstm) model with intermediate variables for weather forecasting. Proc Comput Sci 135:89–98

Satyanarayanan M (2017) The emergence of edge computing. Computer 50(1):30–39

Satyanarayanan M, Bahl P, Caceres R, Davies N (2009) The case for VM-based cloudlets in mobile computing. IEEE Pervasive Comput 8(4):14–23

Seide F, Agarwal A (2016) CNTK: Microsoft’s open-source deep-learning toolkit. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 2135–2135

Simoens P, Xiao Y, Pillai P, Chen Z, Ha K, Satyanarayanan M (2013) Scalable crowd-sourcing of video from mobile devices. In: Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services, pp 139–152. ACM

Zamora-Izquierdo MA, Santa J, Martínez JA, Martínez V, Skarmeta AF (2019) Smart farming iot platform based on edge and cloud computing. Biosyst Eng 177:4–17

Zaytar MA, El Amrani C (2016) Sequence to sequence weather forecasting with long short-term memory recurrent neural networks. Int Comput Appl 143(11):7–11

Zhang N, Wang M, Wang N (2002) Precision agriculture a worldwide overview. Comput Electron Agric 36(2–3):113–132

Zhou Z, Chen X, Li E, Zeng L, Luo K, Zhang J (2019) Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proc IEEE 107(8):1738–1762

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