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Predicting Early Crop Production by Analysing Prior Environment Factors

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


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.


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

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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.

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Correspondence to Rashedur M. Rahman .

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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.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49072-4

  • Online ISBN: 978-3-319-49073-1

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