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Land Suitability Prognostic Model for Crop Planting Using Data Mining Technique
International Journal of Theoretical and Applied Mathematics
Volume 6, Issue 3, June 2020, Pages: 31-38
Received: Apr. 27, 2020; Accepted: May 20, 2020; Published: Jul. 13, 2020
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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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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.
Classification Model, Suitability, Agro-specialists, Membership Function, Fuzzy Logic
To cite this article
Olajide Blessing Olajide, Olawale Olaniyi, Ngozi Chidozie Egejuru, Peter Adebayo Idowu, Land Suitability Prognostic Model for Crop Planting Using Data Mining Technique, International Journal of Theoretical and Applied Mathematics. Vol. 6, No. 3, 2020, pp. 31-38. doi: 10.11648/j.ijtam.20200603.11
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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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