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International Conference on Advances in Information and Communication Technology

ICTA 2016: Advances in Information and Communication Technology pp 470–479Cite as

Predicting Early Crop Production by Analysing Prior Environment Factors

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 538)

Abstract

Bangladesh has an agriculture dependent economy and hence prediction of agricultural production is of great importance to us. In this research we develop a model that considers and analyzes weather and climate prior to specific crop plantation and maps a correlation between these two. It allows us to provide information about the crop state, in quantity and quality with the possibility of early warnings so that timely interventions can be undertaken. The approach advocated in this paper is to help the people with food security and early warning system.

Keywords

  • Data mining
  • Adaptive learning
  • Machine learning
  • Prediction
  • Agriculture
  • Soft computing
  • Environment

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References

  1. Rahman, M., Haque, N., Rahman, R.: Application of Data Mining Tools for Rice Yield Prediction on Clustered Regions of Bangladesh. In: 17th IEEE International Conference on Computer and Information Technology (ICCIT), pp. 8–13. Daffodil International University, Dhaka, Bangladesh (2016)

    Google Scholar 

  2. Bhargavi, P., Jyothi, S.: Applying Naive Bayes Data Mining technique for classification of agricultural land soils. In: International Journal of Computer Science and Network Security, pp. 117–122 (2009)

    Google Scholar 

  3. Ramesh, D., Vardhan, B.: Data mining techniques and applications to agricultural yield data. Int. J. Adv. Res. Comput. Commun. Eng. 2, 3477–3480 (2013)

    Google Scholar 

  4. Sha, Z., Zhang, M.: Development of web-based decision support system for field-based crop management. In: Geographic Information Systems, pp. 1–4 (2007)

    Google Scholar 

  5. Mateo, M., Leung, C.: Design and development of a prototype system for detecting abnormal weather observations. In: Proceedings of the 2008 C3S2E conference on - C3S2E 2008 (2008)

    Google Scholar 

  6. Bangladesh Agricultural Research Council (BARC)-Government of the People’s Republic of Bangladesh, Barc.gov.bd (2016). http://barc.gov.bd/. Accessed 01 May 2016

  7. Bangladesh bureau of Statistics (BBS). http://www.bbs.gov.bd/. Accessed 01 May 2016

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Acknowledgments

We would not complete our research paper without having the support of the organizations named Bangladesh Agricultural Research Council and Bangladesh Bureau of Statistics. We would like to extend our sincere gratitude to those organizations.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, North South University, Plot-15, Block-B, Bashundhara, Dhaka, Bangladesh

    Tousif Osman, Shahreen Shahjahan Psyche, MD Rafik Kamal, Fouzia Tamanna, Farzana Haque & Rashedur M. Rahman

Authors
  1. Tousif Osman
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  2. Shahreen Shahjahan Psyche
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  3. MD Rafik Kamal
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  4. Fouzia Tamanna
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  5. Farzana Haque
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  6. Rashedur M. Rahman
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Corresponding author

Correspondence to Rashedur M. Rahman .

Editor information

Editors and Affiliations

  1. School of Information Science、Area of Human Life Design , Japan Advanced Institute of Science and Technology, Nomi-shi, Ishikawa, Japan

    Masato Akagi

  2. Department of Computer Science, VNU University of Engineering and Technology, Hanoi, Vietnam

    Thanh-Thuy Nguyen

  3. Faculty of Information Technology, Thai Nguyen University of Information and Communication Technology, Thai Nguyen, Vietnam

    Duc-Thai Vu

  4. Thai Nguyen University of Information and Communication Technology, Thai Nguyen, Vietnam

    Trung-Nghia Phung

  5. School of Knowledge Science、Area of Knowledge Management , Japan Advanced Institute of Science and Technology, Nomi-shi, Ishikawa, Japan

    Van-Nam Huynh

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© 2017 Springer International Publishing AG

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Cite this paper

Osman, T., Psyche, S.S., Kamal, M.R., Tamanna, F., Haque, F., Rahman, R.M. (2017). Predicting Early Crop Production by Analysing Prior Environment Factors. In: Akagi, M., Nguyen, TT., Vu, DT., Phung, TN., Huynh, VN. (eds) Advances in Information and Communication Technology. ICTA 2016. Advances in Intelligent Systems and Computing, vol 538. Springer, Cham. https://doi.org/10.1007/978-3-319-49073-1_51

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  • DOI: https://doi.org/10.1007/978-3-319-49073-1_51

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49072-4

  • Online ISBN: 978-3-319-49073-1

  • eBook Packages: EngineeringEngineering (R0)

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