Skip to main content

Advertisement

Springer Nature Link
Log in

The analysis of agricultural Internet of things product marketing by deep learning

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

This study aims to promote the development of agricultural Internet of Things (AIoT) products. Although the general recurrent neural network (RNN) provides a more scientific approach for market forecasting, the accuracy of forecasting varies greatly because market data are affected by many factors. RNN is analyzed combined with the characteristics of IoT products. Through the analysis of the advantages and disadvantages of previous prediction models, long short-term memory (LSTM) is used to optimize the system model. Additionally, the optimized LSTM prediction model is used for hyper-parameter analysis of IoT marketing products. The mean absolute error of the developed LSTM prediction model reaches 303.3112, and the root-mean-square error reaches 397.1752. The prediction trend presented by the designed LSTM prediction model is consistent with the experimental data, which proves that the model has high prediction accuracy. The model is used to analyze future sales of Agricultural IoT (AIoT) products. The strength, weaknesses, opportunities and threats analysis method is used to analyze the AIoT product manufacturers. The AIoT product company has developed a marketing strategy that is in line with the company's development. This strategy promotes the development of IoT agricultural products and modern agricultural production.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
€32.70 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (France)

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Data availability

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

References

  1. Liang TP, Liu YH (2018) Research landscape of business intelligence and big data analytics: a bibliometrics study. Expert Syst Appl 111:2–10

    Article  Google Scholar 

  2. Torres R, Sidorova A, Jones MC (2018) Enabling firm performance through business intelligence and analytics: a dynamic capabilities perspective. Inf Manag 55(7):822–839

    Article  Google Scholar 

  3. Jaklič J, Grublješič T, Popovič A (2018) The role of compatibility in predicting business intelligence and analytics use intentions. Int J Inf Manag 43:305–318

    Article  Google Scholar 

  4. Anand JV (2020) A methodology of atmospheric deterioration forecasting and evaluation through data mining and business intelligence. J Ubiquitous Comput Commun Technol (UCCT) 2(02):79–87

    Google Scholar 

  5. Jin DH, Kim HJ (2018) Integrated understanding of big data, big data analysis, and business intelligence: a case study of logistics. Sustainability 10(10):3778

    Article  Google Scholar 

  6. Vashishtha G, Kumar R (2021) Autocorrelation energy and aquila optimizer for MED filtering of sound signal to detect bearing defect in Francis turbine. Meas Sci Technol 33(1):015006

    Article  Google Scholar 

  7. Chauhan S, Vashishtha G, Kumar A (2021) A symbiosis of arithmetic optimizer with slime mould algorithm for improving global optimization and conventional design problem. J Supercomput 78:6234–6274

    Article  Google Scholar 

  8. Vashishtha G, Kumar R (2022) An amended grey wolf optimization with mutation strategy to diagnose bucket defects in Pelton wheel. Measurement 187:110272

    Article  Google Scholar 

  9. Chauhan S, Singh M, Aggarwal AK (2021) Bearing defect identification via evolutionary algorithm with adaptive wavelet mutation strategy. Measurement 179:109445

    Article  Google Scholar 

  10. Chi T, Chen M (2019) A frequency hopping method for spatial RFID/WiFi/Bluetooth scheduling in agricultural IoT. Wirel Netw 25(2):805–817

    Article  Google Scholar 

  11. Zhang X, Cao Z, Dong W (2020) Overview of Edge Computing in the Agricultural IoT: Key Technologies, Applications. Challenges IEEE Access 8:141748–141761

    Article  Google Scholar 

  12. Ma T, Sun CH, Li WY et al (2019) Design and implementation of trusted traceability system for agricultural products origin based on NB-IoT. J Agric Sci Technol (Beijing) 21(12):58–67

    Google Scholar 

  13. Elavarasan RM, Afridhis S, Vijayaraghavan RR et al (2020) SWOT analysis: a framework for comprehensive evaluation of drivers and barriers for renewable energy development in significant countries. Energy Rep 6:1838–1864

    Article  Google Scholar 

  14. Hajizadeh Y (2019) Machine learning in oil and gas; a SWOT analysis approach. J Pet Sci Eng 176:661–663

    Article  Google Scholar 

  15. Namugenyi C, Nimmagadda SL, Reiners T (2019) Design of a SWOT analysis model and its evaluation in diverse digital business ecosystem contexts. Procedia Comput Sci 159:1145–1154

    Article  Google Scholar 

  16. Irfan M, Hao Y, Panjwani MK et al (2020) Competitive assessment of South Asia’s wind power industry: SWOT analysis and value chain combined model. Energy Strategy Rev 32:100540

    Article  Google Scholar 

  17. Phadermrod B, Crowder RM, Wills GB (2019) Importance-performance analysis based SWOT analysis. Int J Inf Manag 44:194–203

    Article  Google Scholar 

  18. Sebt MV, Ghasemi SH, Mehrkian SS (2021) Predicting the number of customer transactions using stacked LSTM recurrent neural networks. Soc Netw Anal Min 11(1):1–13

    Article  Google Scholar 

  19. Waheeb W, Ghazali R (2019) A novel error-output recurrent neural network model for time series forecasting. Neural Comput Appl 32:9621–9647

    Article  Google Scholar 

  20. Hewamalage H, Bergmeir C, Bandara K (2021) Recurrent neural networks for time series forecasting: current status and future directions. Int J Forecast 37(1):388–427

    Article  Google Scholar 

  21. Bandara K, Bergmeir C, Smyl S (2020) Forecasting across time series databases using recurrent neural networks on groups of similar series: a clustering approach. Expert Syst Appl 140:112896

    Article  Google Scholar 

  22. Gu Q, Lu N, Liu L (2019) A novel recurrent neural network algorithm with long short-term memory model for futures trading. J Intell Fuzzy Syst 37(4):4477–4484

    Article  Google Scholar 

  23. Kurumatani K (2020) Time series forecasting of agricultural product prices based on recurrent neural networks and its evaluation method. SN Appl Sci 2(8):1–17

    Article  Google Scholar 

  24. Abbasimehr H, Shabani M, Yousefi M (2020) An optimized model using LSTM network for demand forecasting. Comput Ind Eng 143:106435

    Article  Google Scholar 

  25. Kumar R, Kumar P, Kumar Y (2021) Integrating big data driven sentiments polarity and ABC-optimized LSTM for time series forecasting. Multimed Tools Appl. https://doi.org/10.1007/s11042-021-11029-1

    Article  Google Scholar 

  26. Li Y, Dai W (2020) Bitcoin price forecasting method based on CNN-LSTM hybrid neural network model. J Eng 2020(13):344–347

    Article  Google Scholar 

  27. Sunil CK, Jaidhar CD, Patil N (2020) Empirical study on multi convolutional layer-based convolutional neural network classifier for plant leaf disease detection. In: IEEE 15th International Conference on Industrial and Information Systems (ICIIS), pp 460–465. https://doi.org/10.1109/ICIIS51140.2020.9342729

  28. Baek Y, Kim HY (2018) ModAugNet: a new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module. Expert Syst Appl 113:457–480

    Article  Google Scholar 

  29. Lin Y, Yan Y, Xu J et al (2021) Forecasting stock index price using the CEEMDAN-LSTM model. N Am J Econ Finance 57:101421

    Article  Google Scholar 

  30. Chang Z, Zhang Y, Chen W (2019) Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform. Energy 187:115804

    Article  Google Scholar 

  31. Ji Y, Liew AWC, Yang L (2021) A novel improved particle swarm optimization with long-short term memory hybrid model for stock indices forecast. IEEE Access 9:23660–23671

    Article  Google Scholar 

  32. Abbasimehr H, Paki R (2021) Improving time series forecasting using LSTM and attention models. J Ambient Intell Hum Comput 13:673–691

    Article  Google Scholar 

  33. Jahangir H, Tayarani H, Gougheri SS et al (2020) Deep learning-based forecasting approach in smart grids with microclustering and bidirectional LSTM network. IEEE Trans Ind Electron 68(9):8298–8309

    Article  Google Scholar 

  34. Chauhan S, Singh M, Aggarwal AK (2021) Design of a two-channel quadrature mirror filter bank through a diversity-driven multi-parent evolutionary algorithm. Circuits Syst Signal Process 40(7):3374–3394

    Article  Google Scholar 

  35. Vashishtha G, Kumar R (2021) An effective health indicator for the Pelton wheel using a Levy flight mutated genetic algorithm. Meas Sci Technol 32(9):094003

    Article  Google Scholar 

  36. Xiangxue W, Lunhui X, Kaixun C (2019) Data-driven short-term forecasting for urban road network traffic based on data processing and LSTM-RNN. Arab J Sci Eng 44(4):3043–3060

    Article  Google Scholar 

  37. Sunil CK, Jaidhar CD, Patil N (2021) Cardamom plant disease detection approach using EfficientNetV2. IEEE Access 10:789–804

    Google Scholar 

Download references

Acknowledgements

The authors acknowledge the help from the university colleagues.

Funding

This research received no external funding.

Author information

Authors and Affiliations

  1. School of Economics and Management, Zhejiang University of Water Resources and Electric Power, Hangzhou, 310000, China

    Qiuyan Liu

  2. School of Economics and Business, Shanghai Urban Construction Vocational College, Shanghai, 200000, China

    Xuan Zhao

  3. School of Economics and Management, China Jiliang University, Hangzhou, 310000, China

    Kaihan Shi

Authors
  1. Qiuyan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xuan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kaihan Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xuan Zhao.

Ethics declarations

Conflict of interest

All 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

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Q., Zhao, X. & Shi, K. The analysis of agricultural Internet of things product marketing by deep learning. J Supercomput 79, 4602–4621 (2023). https://doi.org/10.1007/s11227-022-04817-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04817-5

Keywords

Search

Navigation