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Land Suitability Prognostic Model for Crop Planting Using Data Mining Technique

Received: 27 April 2020     Accepted: 20 May 2020     Published: 13 July 2020
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Abstract

This study aims to formulate a classification model which farmers can use to determine the suitability of a land for supporting cultivation based on information about identified factors. Structured interview with farmers and agro-specialists were conducted in order to identify the factors associated with the classification of land suitability. Fuzzy membership function was used to formulate the input and output variables of the classification model for land suitability based on the risk factors identified. The model was simulated using MATLAB® R2015b -Fuzzy Logic Tool. The results showed that 7 risk factors were associated with the classification of the suitability of land for crop planting. The risk factors identified are annual rainfall, months of dry season, relative humidity, abundance of clay soil, abundance of sand soil, abundance of organic carbon and pH value of soil on land. 2 and 3 triangular membership functions were appropriate for the formulation of the linguistic variables of the factors using appropriate linguistic variables while the target suitability of land was formulated using four triangular membership functions for the linguistic variables unsuitable, fairly suitable, moderately suitable and highly suitable. 288 inferred rules were formulated using IF-THEN statements which adopted the values of the factors as antecedent and the suitability of land for planting crops as the consequent part of each rule. This study concluded that based on the assessment of information about the factors associated with the classification of land suitability a reasonable conclusion can be made about the possible use of land.

Published in International Journal of Theoretical and Applied Mathematics (Volume 6, Issue 3)
DOI 10.11648/j.ijtam.20200603.11
Page(s) 31-38
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Classification Model, Suitability, Agro-specialists, Membership Function, Fuzzy Logic

References
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[7] Hughes P, McBratney A., and Minasny B., “End members, end points and extragrades in numerical soil classification,” Geoderma, vol. 226-227, pp. 365–375, 2014.
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[11] Yiping P., Li Z., Yueming H., and Guangxing (2019): “Prediction of Soil Nutrient Contents using Visible and Near-infrared Reflectance Spectroscopy”, in isprs International Journal of Geo-information, pp 1-18.
[12] Bejo S., Husin N., Ahmad F., Muhamad S., Mohd K., and Desa A. (2019): “Effect of Basal Stem Rot on Oil Palm Inter-frond Angles for Different Severity Levels”, Journals of Advanced Agricultural Technologies 6 (2): 113-117.
[13] Ishaq I., Alias M., Kadir J., andKasawani I. (2016):“Detection of basal stem rot disease at oil palm plantations using sonic tomography,”Journal of Sustainability Science and Management, 9 (2): 52-57.
[14] Zhu A, Hudson B., Burt J., Lubich K. and Simonson D. (2014): “Soil Mapping Using GIS, Expert Knowledge and Fuzzy Logic, pp 12-16.
[15] Samir H., Kaddour D. and Mohamed B. (2018): “Fuzzy Logic Expert System for Classifying Solonchaks of Algeria, Hindawi Applied and Environmental Soil Science, 1-11.
Cite This Article
  • APA Style

    Olajide Blessing Olajide, Olawale Olaniyi, Ngozi Chidozie Egejuru, Peter Adebayo Idowu. (2020). Land Suitability Prognostic Model for Crop Planting Using Data Mining Technique. International Journal of Theoretical and Applied Mathematics, 6(3), 31-38. https://doi.org/10.11648/j.ijtam.20200603.11

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    ACS Style

    Olajide Blessing Olajide; Olawale Olaniyi; Ngozi Chidozie Egejuru; Peter Adebayo Idowu. Land Suitability Prognostic Model for Crop Planting Using Data Mining Technique. Int. J. Theor. Appl. Math. 2020, 6(3), 31-38. doi: 10.11648/j.ijtam.20200603.11

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    AMA Style

    Olajide Blessing Olajide, Olawale Olaniyi, Ngozi Chidozie Egejuru, Peter Adebayo Idowu. Land Suitability Prognostic Model for Crop Planting Using Data Mining Technique. Int J Theor Appl Math. 2020;6(3):31-38. doi: 10.11648/j.ijtam.20200603.11

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  • @article{10.11648/j.ijtam.20200603.11,
      author = {Olajide Blessing Olajide and Olawale Olaniyi and Ngozi Chidozie Egejuru and Peter Adebayo Idowu},
      title = {Land Suitability Prognostic Model for Crop Planting Using Data Mining Technique},
      journal = {International Journal of Theoretical and Applied Mathematics},
      volume = {6},
      number = {3},
      pages = {31-38},
      doi = {10.11648/j.ijtam.20200603.11},
      url = {https://doi.org/10.11648/j.ijtam.20200603.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijtam.20200603.11},
      abstract = {This study aims to formulate a classification model which farmers can use to determine the suitability of a land for supporting cultivation based on information about identified factors. Structured interview with farmers and agro-specialists were conducted in order to identify the factors associated with the classification of land suitability. Fuzzy membership function was used to formulate the input and output variables of the classification model for land suitability based on the risk factors identified. The model was simulated using MATLAB® R2015b -Fuzzy Logic Tool. The results showed that 7 risk factors were associated with the classification of the suitability of land for crop planting. The risk factors identified are annual rainfall, months of dry season, relative humidity, abundance of clay soil, abundance of sand soil, abundance of organic carbon and pH value of soil on land. 2 and 3 triangular membership functions were appropriate for the formulation of the linguistic variables of the factors using appropriate linguistic variables while the target suitability of land was formulated using four triangular membership functions for the linguistic variables unsuitable, fairly suitable, moderately suitable and highly suitable. 288 inferred rules were formulated using IF-THEN statements which adopted the values of the factors as antecedent and the suitability of land for planting crops as the consequent part of each rule. This study concluded that based on the assessment of information about the factors associated with the classification of land suitability a reasonable conclusion can be made about the possible use of land.},
     year = {2020}
    }
    

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  • TY  - JOUR
    T1  - Land Suitability Prognostic Model for Crop Planting Using Data Mining Technique
    AU  - Olajide Blessing Olajide
    AU  - Olawale Olaniyi
    AU  - Ngozi Chidozie Egejuru
    AU  - Peter Adebayo Idowu
    Y1  - 2020/07/13
    PY  - 2020
    N1  - https://doi.org/10.11648/j.ijtam.20200603.11
    DO  - 10.11648/j.ijtam.20200603.11
    T2  - International Journal of Theoretical and Applied Mathematics
    JF  - International Journal of Theoretical and Applied Mathematics
    JO  - International Journal of Theoretical and Applied Mathematics
    SP  - 31
    EP  - 38
    PB  - Science Publishing Group
    SN  - 2575-5080
    UR  - https://doi.org/10.11648/j.ijtam.20200603.11
    AB  - This study aims to formulate a classification model which farmers can use to determine the suitability of a land for supporting cultivation based on information about identified factors. Structured interview with farmers and agro-specialists were conducted in order to identify the factors associated with the classification of land suitability. Fuzzy membership function was used to formulate the input and output variables of the classification model for land suitability based on the risk factors identified. The model was simulated using MATLAB® R2015b -Fuzzy Logic Tool. The results showed that 7 risk factors were associated with the classification of the suitability of land for crop planting. The risk factors identified are annual rainfall, months of dry season, relative humidity, abundance of clay soil, abundance of sand soil, abundance of organic carbon and pH value of soil on land. 2 and 3 triangular membership functions were appropriate for the formulation of the linguistic variables of the factors using appropriate linguistic variables while the target suitability of land was formulated using four triangular membership functions for the linguistic variables unsuitable, fairly suitable, moderately suitable and highly suitable. 288 inferred rules were formulated using IF-THEN statements which adopted the values of the factors as antecedent and the suitability of land for planting crops as the consequent part of each rule. This study concluded that based on the assessment of information about the factors associated with the classification of land suitability a reasonable conclusion can be made about the possible use of land.
    VL  - 6
    IS  - 3
    ER  - 

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Author Information
  • Department of Computer Science, Federal University, Wukari, Nigeria

  • Department of Computer Science, Tai Solarin University of Education, Ijagun, Nigeria

  • Department of Computer Science, Hallmark University, Ijebu Itele, Nigeria

  • Department of Computer Science and Engineering, Obafemi Awolowo University, Ile Ife, Nigeria

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