SpringerLink
Log in

OntoFusionCrop: An Ontology Centric Approach for Crop Recommendation Based on Bagging and Semantic Alignment

  • Conference paper
  • First Online:
Digital Technologies and Applications (ICDTA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 455))

Included in the following conference series:

Abstract

Agriculture is a crucial source of livelihood. There are many farmers who take up farming as their main source of income. The major difficulty present among farmers is that their crop selection is not based on the soil and weather conditions. Thus, it is essential for farmers to know the necessary information regarding the different facets of crop production, best agricultural practices, suitable crops for a specific soil type to be grown in a particular weather, etc. A crop recommender system, which has accurate and precise information, serves as a means for the farmers to farm appropriate crops and get the best yield. This paper proposes an ontology-based recommender system to provide the appropriate details regarding crops and other information associated with them. The proposed OntoFusionCrop uses ontology cluster that includes soil ontology, crop ontology, geographical ontology and agricultural ontology. This strategy uses the crop recommendation dataset from Kaggle which is classified using bagging. The classified instances along with the ontology cluster are semantically aligned using spider monkey optimization algorithm from which we get facts after suitable verification. The query is asked by the user and the recommender system gives the output. The proposed model achieved 93.87% accuracy and A minimum of 0.05 FDR compared to the existing models.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 181.89
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 235.39
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Kulkarni, N.H., Srinivasan, G.N., Sagar, B.M., Cauvery, N.K.: Improving crop productivity through a crop recommendation system using ensembling technique. In: 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions (2018)

    Google Scholar 

  2. Doshi, Z., Nadkarni, S., Agrawal, R., Shah, N.: AgroConsultant: intelligent crop recommendation system using machine learning algorithms. In: 2018 Fourth International Conference on Computing Communication Control and Automation (2018)

    Google Scholar 

  3. Kuanr, M., Rath, B.K., Mohanty, S.: Crop recommender system for the farmers using Mamdani fuzzy inference model. Int. J. Eng. Technol. 7(4.15), 277 (2018)

    Google Scholar 

  4. Bangaru Kamatchi, S., Parvathi, R.: Improvement of crop production using recommender system by weather forecasts. Procedia Comput. Sci. 165, 724–732 (2019)

    Article  Google Scholar 

  5. Konaté, J., Diarra, A.G., Diarra, S.O., Diallo, A.: SyrAgri: a recommender system for agriculture in Mali. Information 11, 561 (2018)

    Google Scholar 

  6. Suresh, G., Senthil Kumar, A., Lekashri, S., Manikandan, R.: Efficient crop yield recommendation system using machine learning for digital farming. Int. J. Modern Agric. 10(1), 906–914 (2021)

    Google Scholar 

  7. Kumar, A., Sarkar, S., Pradhan, C.: Recommendation system for crop identification and pest control technique in agriculture. In: 2019 International Conference on Communication and Signal Processing (2019)

    Google Scholar 

  8. Bondre, D.A., Mahagaonkar, S.: Prediction of crop yield and fertilizer recommendation using machine learning algorithms. J. Eng. Appl. Sci. Technol. 4(5), 371–376 (2019)

    Google Scholar 

  9. Deepak, G., Kasaraneni, D.: OntoCommerce: an ontology focused semantic framework for personalised product recommendation for user targeted e-commerce. Int. J. Comput. Aided Eng. Technol. 11(4–5), 449–466 (2019)

    Google Scholar 

  10. Deepak, G., Rooban, S., Santhanavijayan, A.: A knowledge centric hybridized approach for crime classification incorporating deep bi-LSTM neural network. Multimedia Tools Appl. 80(18), 28061–28085 (2021). https://doi.org/10.1007/s11042-021-11050-4

    Article  Google Scholar 

  11. Deepak, G., Santhanavijayan, A.: UQSCM-RFD: a query–knowledge interfacing approach for diversified query recommendation in semantic search based on river flow dynamics and dynamic user interaction. Neural Comput. Appl. 1–25 (2021)

    Google Scholar 

  12. Krishnan, N., Deepak, G.: Towards a novel framework for trust driven web URL recommendation incorporating semantic alignment and recurrent neural network. In: 2021 7th International Conference on Web Research (ICWR), pp. 232–237. IEEE (2021)

    Google Scholar 

  13. Roopak, N., Deepak, G.: OntoKnowNHS: ontology driven knowledge centric novel hybridised semantic scheme for image recommendation using knowledge graph. In: Villazón-Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S., Goyal, A., Jabbar, M. (eds.) Iberoamerican Knowledge Graphs and Semantic Web Conference, KGSWC 2021. Communications in Computer and Information Science, vol. 1459, pp. 138–152. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-91305-2_11

  14. Ojha, R., Deepak, G.: Metadata driven semantically aware medical query expansion. In: Villazón-Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S., Goyal, A., Jabbar, M.A. (eds.) KGSWC 2021. CCIS, vol. 1459, pp. 223–233. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-91305-2_17

    Chapter  Google Scholar 

  15. Surya, D., Deepak, G.: USWSBS: user-centric sensor and web service search for IoT application using bagging and sunflower optimization. In: Noor, A., Sen, A., Trivedi, G. (eds.) Proceedings of Emerging Trends and Technologies on Intelligent Systems. ETTIS 2021. Advances in Intelligent Systems and Computing, vol. 1371, pp. 349–359. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-3097-2_29

  16. Arulmozhivarman, M., Deepak, G.: OWLW: ontology focused user centric architecture for web service recommendation based on LSTM and Whale optimization. In: Musleh Al-Sartawi, A.M.A., Razzaque, A., Kamal, M.M. (eds.) EAMMIS 2021. LNNS, vol. 239, pp. 334–344. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77246-8_32

    Chapter  Google Scholar 

  17. Adithya, V., Deepak, G., Santhanavijayan, A.: HCODF: hybrid cognitive ontology driven framework for socially relevant news validation. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 731–739. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_66

    Chapter  Google Scholar 

  18. Rithish, H., Deepak, G., Santhanavijayan, A.: Automated assessment of question quality on online community forums. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 791–800. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_72

    Chapter  Google Scholar 

  19. Aditya, S., Muhil Aditya, P., Deepak, G., Santhanavijayan, A.: IIMDR: intelligence integration model for document retrieval. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 707–717. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_64

    Chapter  Google Scholar 

  20. Tiwari, S., Al-Aswadi, F.N., Gaurav, D.: Recent trends in knowledge graphs: theory and practice. Soft Comput. 25(13), 8337–8355 (2021). https://doi.org/10.1007/s00500-021-05756-8

    Article  Google Scholar 

  21. Abhishek, K., Pratihar, V., Shandilya, S.K., Tiwari, S., Ranjan, V.K., Tripathi, S.: An intelligent approach for mining knowledge graphs of online news. Int. J. Comput. Appl. 1–9 (2021)

    Google Scholar 

  22. Krishnan, N., Deepak, G.: KnowSum: knowledge inclusive approach for text summarization using semantic alignment. In: 2021 7th International Conference on Web Research (ICWR), pp. 227–231. IEEE (2021)

    Google Scholar 

  23. Deepak, G., Gulzar, Z., Leema, A.A.: An intelligent system for modeling and evaluation of domain ontologies for Crystallography as a prospective domain with a focus on their retrieval. Comput. Electr. Eng. 96, 107604 (2021)

    Article  Google Scholar 

  24. Chatrati, S.P., et al.: Smart home health monitoring system for predicting type 2 diabetes and hypertension. J. King Saud Univ. Comput. Inf. Sci. (2020)

    Google Scholar 

  25. Deepak, G., Priyadarshini, J.S.: Personalized and enhanced hybridized semantic algorithm for web image retrieval incorporating ontology classification, strategic query expansion, and content-based analysis. Comput. Electr. Eng. 72, 14–25 (2018)

    Article  Google Scholar 

  26. Singh, S., Deepak, G.: Towards a knowledge centric semantic approach for text summarization. In: Shukla, S., Unal, A., Varghese Kureethara, J., Mishra, D.K., Han, D.S. (eds.) Data Science and Security. LNNS, vol. 290, pp. 1–9. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-4486-3_1

    Chapter  Google Scholar 

  27. Anantha Reddy, D., Dadore, B., Watekar, A.: Crop recommendation system to maximize crop yield in Ramtek region using machine learning. Int. J. Sci. Res. Sci. Technol. (2019)

    Google Scholar 

  28. Pudumalar, S., Ramanujam, E., Rajashree, R.H., Kavya, C., Kiruthika, T., Nisha, J.: Crop recommendation system for precision agriculture. In: 2016 Eighth International Conference on Advanced Computing (2017)

    Google Scholar 

  29. Gavas, P., Jamsandekar, M., Meghani, P., Sabnis, S.: Crop recommendation system. Int. J. Creative Res. Thoughts

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India

    Aparna Chandramouli

  2. Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, India

    Gerard Deepak

Authors
  1. Aparna Chandramouli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gerard Deepak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gerard Deepak .

Editor information

Editors and Affiliations

  1. Ecole Nationale des Sciences Appliquées, Fez, Morocco

    Saad Motahhir

  2. Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Badre Bossoufi

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chandramouli, A., Deepak, G. (2022). OntoFusionCrop: An Ontology Centric Approach for Crop Recommendation Based on Bagging and Semantic Alignment. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2022. Lecture Notes in Networks and Systems, vol 455. Springer, Cham. https://doi.org/10.1007/978-3-031-02447-4_22

Download citation

Publish with us

Policies and ethics

Search

Navigation