Region Centric Multi Feature Growth Analysis Model for Efficient Plant Selection and Recommendation | SN Computer Science Skip to main content

Advertisement

Springer Nature Link
Log in

Region Centric Multi Feature Growth Analysis Model for Efficient Plant Selection and Recommendation

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Several approaches have been named in earlier articles regarding the problem of plant selection and recommendation. Unfortunately, these methods are ineffective in achieving successful plant selection and recommendations for high performance. To solve this issue, a competent Region Centric Multi Feature Growth Analysis Model (RMFGAM) is presented in this paper. The model considers different features like CFSI (Climate–Fluid–Soil–Industry) towards the problem. Also, the model preprocesses the available plant traces and extracts such features. Further, the traces are grouped and based on the features being extracted, the model applies climate centric plant growth analysis, fluid centric plant growth analysis, soil centric plant growth analysis and industry centric plant growth analysis. Each analysis measures influence of such features in regional manner. Using the influence measures computed, the method computes the plant selection weight for various plants. Based on the plant selection weight, the plants are selected and ranked to produce the recommendation. The proposed method develops the performance of plant selection and recommendation.

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
¥17,985 /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 (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data Availability

The dataset generated and analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. van Klompenburg T, Kassahun A, Catal C. Crop yield prediction using machine learning: a systematic literature review. Comput Electr Agric. 2020;177: 105709. https://doi.org/10.1016/j.compag.2020.105709.

    Article  Google Scholar 

  2. Shook J, Gangopadhyay T. Crop yield prediction integrating genotype and weather variables using deep learning. J Pone. 2017;16:e252402.

    Google Scholar 

  3. Kavita M, Mathur P. Crop yield estimation in india using machine learning. IEEE Int Conf Comput Commun Autom (ICCCA). 2020. https://doi.org/10.1109/ICCCA49541.2020.9250915.

    Article  Google Scholar 

  4. Haki S, Wang L, Archontoulis SV. A CNN-RNN framework for crop yield prediction. Front Plant Sci. 2020;10:1750. https://doi.org/10.3389/fpls.2019.01750.

    Article  Google Scholar 

  5. Pallavi K, Pallavi P, Shrilatha S, Sushma, Sowmya S. Crop yield forecasting using data mining. Glob Trans Proc. 2021. https://doi.org/10.1016/j.gltp.2021.08.008.

    Article  Google Scholar 

  6. Bhagyashree L. Crop yield prediction in smartfarm agriculture system for farmers using IoT. Int J Adv Sci Technol. 2020;29(7):5165–75.

    Google Scholar 

  7. Khalid AA-G. Prediction of potato crop yield using precision agriculture techniques. PLoS ONE. 2016;11(9):2219. https://doi.org/10.1371/journal.pone.0162219.

    Article  MathSciNet  Google Scholar 

  8. Sujatha R, Isakki P. A study on crop yield forecasting using classification techniques. Int Conf Comput Technol Intell Data Eng. 2016. https://doi.org/10.1109/ICCTIDE.2016.7725357.

    Article  Google Scholar 

  9. Singh R, Srivastava S, Mishra R. AI and IoT based monitoring system for increasing the yield in crop production. Int Conf Electr Electr Eng (ICE3). 2020. https://doi.org/10.1109/ICE348803.2020.9122894.

    Article  Google Scholar 

  10. Swarupa Rani A. The impact of data analytics in crop management based on weather conditions. Int J Eng Technol Sci Res. 2017;4:299–308.

    Google Scholar 

  11. Jain N, Kumar A, Garud S, Pradhan V, Kulkarni P. Crop selection method based on various environmental factors using machine learning. Int Res J Eng Technol (IRJET). 2017;4(02):551–8.

    Google Scholar 

  12. Yang MD, Tseng HH, Hsu YC, Tseng WC. Real-time crop classification using edge computing and deep learning. In: 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), 2020, pp. 1–4.

  13. Bhimanpallewar R, Narasinagrao MR. A machine learning approach to assess crop specific suitability for small/marginal scale croplands. Int J Appl Eng Res. 2017;12(23):13966–73.

    Google Scholar 

  14. Ertina SB, Muhammad Z, Zulkifly N, Sutarman. Forcasting plant growth using neural network time series. In: 2019 International Conference of Computer Science and Information Technology (ICoSNIKOM), 2019, pp. 1–5.

  15. Hewage P, Trovati M, Pereira E, et al. Deep learning-based effective fine-grained weather forecasting model. Pattern Anal Appl. 2021;24:343–66. https://doi.org/10.1007/s10044-020-00898-1.

    Article  Google Scholar 

  16. Dhivya E, Durairaj-Vincent PM. Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications. IEEE Access. 2020. https://doi.org/10.1109/ACCESS.2020.2992480.

    Article  Google Scholar 

  17. Nevavuori P, Narra N, Lipping T. Crop yield prediction with deep convolutional neural networks’. Comput Electron Agric. 2019;163:104859.

    Article  Google Scholar 

  18. Kamilaris A, Prenafeta-Boldú FX. Deep learning in agriculture: a survey. Comput Electron Agric. 2018;147:7090.

    Article  Google Scholar 

  19. Rehman TU, Mahmud S, Chang YK, Jin J, Shin J. Current and future applications of statistical machine learning algorithms for agricultural machine vision systems. Comput Electron Agric. 2019;156: 585605.

    Article  Google Scholar 

  20. RaghavendraNayaka P, Ranjan R. An efficient framework for algorithmic metadata extraction over scholarly documents using deep neural networks. SN Comput Sci. 2023;4:341. https://doi.org/10.1007/s42979-023-01776-3.

    Article  Google Scholar 

  21. Kumble L, Patil KK. Evalutionary STBD model for bio-signal compression provisioning in wire-less sensor network. Int Conf Smart Technol Smart Nation. 2017. https://doi.org/10.1109/SmartTechCon.2017.8358634.

    Article  Google Scholar 

  22. RaghavendraNayaka P, Ranjan R. An efficient framework for metadata extraction over scholarly documents using ensemble CNN and BiLSTM technique. Int Conf Innov Technol (INOCON). 2023. https://doi.org/10.1109/INOCON57975.2023.10101029.

    Article  Google Scholar 

  23. Kumble L, Patil KK. An improved data compression framework for wireless sensor networks using stacked convolutional autoencoder (S-CAE). SN Comput Sci. 2023;4:419. https://doi.org/10.1007/s42979-023-01845-7.

    Article  Google Scholar 

Download references

Funding

No funding received for this research.

Author information

Authors and Affiliations

  1. Department of Computer Science, Rajah Serfoji Government College (Autonomous) Thanjavur, Affiliated to Bharathidasan University, Tiruchirapalli, 620024, India

    K. Bommi & K. Mohan Kumar

  2. Department of Computer Science, Government Arts College, Affiliated to Bharathidasan University, Tiruchirappalli, 620022, India

    D. J. Evanjaline

Authors
  1. K. Bommi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. D. J. Evanjaline
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. K. Mohan Kumar
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to K. Bommi.

Ethics declarations

Conflict of Interest

No conflict of interest.

Additional information

Publisher's Note

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

This article is part of the topical collection “Advances in Computational Approaches for Image Processing, Wireless Networks, Cloud Applications and Network Security” guest edited by P. Raviraj, Maode Ma and Roopashree H R.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) 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

Bommi, K., Evanjaline, D.J. & Kumar, K.M. Region Centric Multi Feature Growth Analysis Model for Efficient Plant Selection and Recommendation. SN COMPUT. SCI. 4, 810 (2023). https://doi.org/10.1007/s42979-023-02259-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-02259-1

Keywords