Skip to main content
Log in

Multidimensional agro-economic model with soft-IoT framework

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

In recent times, there has been a great motivation toward research in the field of big data analysis and their incorporation into Internet of Things (IoT). The basic idea behind IoT is to ensure provision of services to clients all over the globe at any point of time from a pool of resources. With increasing volumes of data being handled, processed and stored in recent times, an efficient processing mechanism for these huge volumes of data is available in the form of big data handlers which ensure speedy provision of services without any delay overhead. Hence, big data and IoT put together prove to be the most effective tool and need of the hour for smooth handling and provision of services demanded by clients thus improving the overall quality of service. These two concepts have been effectively applied to developing an intelligent agricultural economic model which is quite heterogeneous in nature thus posing to be a great research challenge. Agro-economic models are quite essential and critical as agriculture forms the backbone of many developing nations across the globe. ANFIS model introduces the necessary intelligence for the big data analytic system to handle the heterogeneous nature of agro-economic input data and provide a suitable prediction. A sample data of five hundred details from five different subsets have been used in the experimental model and prediction of yield computed and compared against recent techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Aazam M, Khan I, Alsaffar AA, Huh E-N (2014) Cloud of things: integrating Internet of Things and cloud computing and the issues involved. In: Proceedings of 11th international Bhurban conference on applied science technology (IBCAST), Islamabad, Pakistan, pp 414–419

  • Batalla JM, Krawiec P (2014) Conception of ID layer performance at the network level for Internet of Things. Pers Ubiquit Comput 18:465–480

    Article  Google Scholar 

  • Cárdenas AA, Manadhata PK, Rajan SP (2013) Big data analytics for security. IEEE Secur Priv 11(6):74–76

    Article  Google Scholar 

  • Choi C, Esposito C, Wang H, Liu Z, Choi J (2018) Intelligent power equipment management based on distributed context-aware inference in smart cities. IEEE Commun Mag 56(7):212–217

    Article  Google Scholar 

  • Cozzolino G (2018) Using semantic tools to represent data extracted from mobile devices. In: 2018 IEEE international conference on information reuse and integration (IRI), IEEE, pp 530–536

  • Fahimifard SM, Salarpour M, Sabouhi M, Shirzady S (2009) Application of ANFIS to agricultural economic variables forecasting case study: poultry retail price. J Artif Intell 2(2):65–72

    Article  Google Scholar 

  • Jagadish HV, Gehrke J, Labrinidis A, Papakonstantinou Y, Patel JM, Ramakrishnan R, Shahabi C (2014) Big data and its technical challenges. Commun ACM 57(7):86–94

    Article  Google Scholar 

  • Khan Z, Anjum A, Kiani SL (2013) Cloud based big data analytics for future cities. In: Proceedings of Conference on utility and cloud computing, IEEE Computer Society, pp 381–386

  • Li C, Wang M (2013) Excerpts from the translation of challenges and opportunities with big data. J E-Sci Technol Appl 4(1):12–18

    Google Scholar 

  • Lokers R, Knapen R, Janssen S, van Randen Y, Jansen J (2016) Analysis of big data technologies for use in agro-environmental. Environ Modell Softw 84:494–504

    Article  Google Scholar 

  • Mohamed N, Al-Jaroodi J (2014) Real-time big data analytics: applications and challenges. In: 2014 international conference on high performance computing & simulation (HPCS), vol 2, pp 305, 310

  • Mohammadi A, Rafiee A, Mohtasebi SS, Rafiee H (2010) Energy inputs—yield relationship and cost analysis of kiwifruit production in Iran. Renew Energy 35:1071–1075

    Article  Google Scholar 

  • Muhammad K, Hamza R, Ahmad J, Lloret J, Wang H, Baik SW (2018) Secure surveillance framework for IoT systems using probabilistic image encryption. IEEE Trans Ind Inf 14(8):3679–3689

    Article  Google Scholar 

  • Nicholls CI, Altieri MA, Dezanet A, Lana M, Feistauer D, Ouriques M (2004) A rapid, farmer-friendly agroecological method to estimate soil quality and crop health in vineyard systems. Biodynamics 8:33–39

    Google Scholar 

  • Pacini C, Wossink A, Giesen G, Vazzana C, Huirne R (2003) Evaluation of sustainability of organic, integrated and conventional farming systems: a farm and field-scale analysis. Agr Ecosyst Environ 95(1):273–288

    Article  Google Scholar 

  • Plageras AP, Psannis KE, Stergiou C, Wang H, Gupta BB (2018) Efficient IoT-based sensor BIG Data collection–processing and analysis in smart buildings. Future Gener Comput Syst 82:349–357

    Article  Google Scholar 

  • RahimiZade M, Madani H, Rezadost S, Mehraban A, Marjaniz A (2007) Energy analysis in ecological farming systems and strategies to increase energy efficiency. Sixth Natl Conf Energy 12(13):1–12

    Google Scholar 

  • Schmolke A, Thorbek P, DeAngelis DL, Grimm V (2010) Ecological models supporting environmental decision making: a strategy for the future. Trends Ecol Evol 25(8):479–486

    Article  Google Scholar 

  • Shih HS, Shyur HJ, Lee ES (2007) An extension of TOPSIS for group decision making. Math Comput Modell 45(7):801–813

    Article  Google Scholar 

  • Song Z, Lazarescu MT, Tomasi R, Lavagno L, Spirito MA (2014) High-level Internet of Things applications development using wireless sensor networks. In: Internet of Things, Springer, Madrid, pp 75–109

  • Sun Y (2019) Analysis for center deviation of circular target under perspective projection. Eng Comput 36(7):2403–2413. https://doi.org/10.1108/EC-09-2018-0431

    Article  Google Scholar 

  • Uckelmann D, Harrison M, Michahelles F (2011) An architecture approach towards the future Internet of Things. Architecting the Internet of Things. Springer, Berlin, pp 1–24

    Chapter  Google Scholar 

  • Varatharajan R, Manogaran G, Priyan MK (2018) A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multimed Tools Appl 77(8):10195–10215

    Article  Google Scholar 

  • Villa F, Athanasiadis IN, Rizzoli AE (2009) Modelling with knowledge: a review of emerging semantic approaches to environmental modelling. Environ Model Softw 24(5):577–587

    Article  Google Scholar 

  • Wang Y-Z, Jin X-L, Chen X-Q (2013) Network big data: present and future. Chin J Comput 6(36):1125–1138

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bin Tian.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, X., Wang, H.H. & Tian, B. Multidimensional agro-economic model with soft-IoT framework. Soft Comput 24, 12187–12196 (2020). https://doi.org/10.1007/s00500-019-04657-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-019-04657-1

Keywords

Navigation