Land Suitability Prognostic Model for Crop Planting Using Data Mining Technique

Olajide Blessing Olajide; Olawale Olaniyi; Ngozi Chidozie Egejuru; Peter Adebayo Idowu

doi:doi:10.11648/j.ijtam.20200603.11

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Land Suitability Prognostic Model for Crop Planting Using Data Mining Technique

Olajide Blessing Olajide, Olawale Olaniyi, Ngozi Chidozie Egejuru, Peter Adebayo Idowu

Published in International Journal of Theoretical and Applied Mathematics (Volume 6, Issue 3)

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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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

Olajide Blessing Olajide

Department of Computer Science, Federal University, Wukari, Nigeria
Olawale Olaniyi

Department of Computer Science, Tai Solarin University of Education, Ijagun, Nigeria
Ngozi Chidozie Egejuru

Department of Computer Science, Hallmark University, Ijebu Itele, Nigeria
Peter Adebayo Idowu

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

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Plain Text BibTeX RIS

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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