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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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dc.contributor.author Guillén-Navarro, Miguel A. es_ES
dc.contributor.author Llanes, Antonio es_ES
dc.contributor.author Imbernón, Baldomero es_ES
dc.contributor.author Martínez-España, Raquel es_ES
dc.contributor.author Bueno-Crespo, Andrés es_ES
dc.contributor.author Cano, Juan-Carlos es_ES
dc.contributor.author Cecilia-Canales, José María es_ES
dc.date.accessioned 2023-09-13T18:00:25Z
dc.date.available 2023-09-13T18:00:25Z
dc.date.issued 2021-01 es_ES
dc.identifier.issn 0920-8542 es_ES
dc.identifier.uri http://hdl.handle.net/10251/196324
dc.description.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 out by advance artificial intelligence (AI) techniques that provide insights never before imagined. The combination of both IoT and AI is giving rise to an emerging trend, called AIoT, which is opening up new paths to bring digitization into the new era. However, there is still a big gap between AI and IoT, which is basically in the computational power required by the former and the lack of computational resources offered by the latter. This is particularly true in rural IoT environments where the lack of connectivity (or low-bandwidth connections) and power supply forces the search for "efficient" alternatives to provide computational resources to IoT infrastructures without increasing power consumption. In this paper, we explore edge computing as a solution for bridging the gaps between AI and IoT in rural environment. We evaluate the training and inference stages of a deep-learning-based precision agriculture application for frost prediction in modern Nvidia Jetson AGX Xavier in terms of performance and power consumption. Our experimental results reveal that cloud approaches are still a long way off in terms of performance, but the inclusion of GPUs in edge devices offers new opportunities for those scenarios where connectivity is still a challenge. es_ES
dc.description.sponsorship 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 under grants RTI2018-096384-B-I00 (AEI/FEDER, UE) and RTC-2017-6389-5. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof The Journal of Supercomputing es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Edge computing es_ES
dc.subject LSTM es_ES
dc.subject Deep learning es_ES
dc.subject Precision Agriculture es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s11227-020-03288-w es_ES
dc.relation.projectID 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/ es_ES
dc.relation.projectID 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/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/f SéNeCa//20813%2FPI%2F18/ es_ES
dc.rights.accessRights Abierto es_ES
dc.date.embargoEndDate 1999-01-01 es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation 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 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s11227-020-03288-w es_ES
dc.description.upvformatpinicio 818 es_ES
dc.description.upvformatpfin 840 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 77 es_ES
dc.relation.pasarela S\425065 es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia es_ES
dc.description.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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references Cass S (2019) Taking AI to the edge: Google’s TPU now comes in a maker-friendly package. IEEE Spectr 56(5):16–17 es_ES
dc.description.references Chen X, Shi Q, Yang L, Xu J (2018) Thriftyedge: Resource-efficient edge computing for intelligent IoT applications. IEEE Netw 32(1):61–65 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references Gondchawar N, Kawitkar R (2016) Iot based smart agriculture. Int J Adv Res Comput Commun Eng 5(6):838–842 es_ES
dc.description.references Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge es_ES
dc.description.references 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 es_ES
dc.description.references Grinblat GL, Uzal LC, Larese MG, Granitto PM (2016) Deep learning for plant identification using vein morphological patterns. Comput Electron Agric 127:418–424 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references Gulli A, Pal S (2017) Deep learning with Keras. Packt Publishing Ltd, Birmingham es_ES
dc.description.references Halawa H, Abdelhafez HA, Boktor A, Ripeanu M (2017) Nvidia Jetson platform characterization. In: EuropEan Conference on Parallel Processing. Springer, pp 92–105 es_ES
dc.description.references Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780 es_ES
dc.description.references Kamilaris A, Prenafeta-Boldu FX (2018) Deep learning in agriculture: a survey. Comput Electron Agric 147:70–90 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references Li Y, Zhao K, Chu X, Liu J (2013) Speeding up k-means algorithm by gpus. J Comput Syst Sci 79(2):216–229 es_ES
dc.description.references Liakos K, Busato P, Moshou D, Pearson S, Bochtis D (2018) Machine learning in agriculture: a review. Sensors 18(8):2674 es_ES
dc.description.references 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 es_ES
dc.description.references Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419 es_ES
dc.description.references Pierpaoli E, Carli G, Pignatti E, Canavari M (2013) Drivers of precision agriculture technologies adoption: a literature review. Proc Technol 8:61–69 es_ES
dc.description.references Qi S, Wan L, Fu B (2018) Multisource and multiuser water resources allocation based on genetic algorithm. J Supercomput, pp 1–9 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references Satyanarayanan M (2017) The emergence of edge computing. Computer 50(1):30–39 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references Zhang N, Wang M, Wang N (2002) Precision agriculture a worldwide overview. Comput Electron Agric 36(2–3):113–132 es_ES
dc.description.references 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 es_ES


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