, Volume 100, Issue 7, pp 689–713 | Cite as

Fuzzy based approach for discovering crops plantation knowledge from huge agro-climatic data respecting climate changes

  • Assem H. Mohammed
  • Ahmed M. Gadallah
  • Hesham A. Hefny
  • M. Hazman


Climate change has noticeable significant impacts on development of most countries because of its direct negative effect on the production and revenue of most crops plantation process. In reality, the ongoing changes in climate variables affect the suitability of planting some crops in their traditional places at their traditional dates. Furthermore, the availability of huge volumes of agro-climatic data that almost incorporates uncertainty increases the complexity of managing and discovering the crops suitable plantation patterns from such data. Accordingly, a need appeared to an efficient approach to handle such uncertainty and to exploit such huge data volume to manage the crops plantation process accurately. This paper presents a fuzzy approach based on Hadoop for discovering crops plantation knowledge from the agro-climatic historical database of the years from 1983 to 2016 of Egypt. Commonly, the proposed approach provides a set of scenarios for plantation dates of each crop with a suitability degree for each scenario. Also, it helps managing crops plantation process from some other aspects such as harvesting dates, candidate diseases and follow up for crops water requirements respecting the data streaming of the prevailing weather data. The proposed approach has been tested on a set of crops with cooperation of researchers from Cairo University and Agricultural Research Center. The results show the added value of the proposed approach against other works respecting the more suitable crops plantation dates, harvesting dates, expected diseases and follow up for crops water requirements. Furthermore, the proposed approach benefits from Hadoop framework capabilities of handling huge amounts of data streamed from weather stations.


Fuzzy set theory Climate change Crops plantation Agro-climatic data Hadoop Prediction 

Mathematics Subject Classification

94D05 (Fuzzy logic; logic of vagueness) 


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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Statistical Studies and ResearchCairo UniversityCairoEgypt
  2. 2.Central Laboratory of Agricultural Expert SystemsAgricultural Research CenterCairoEgypt

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