ew 23(1):

Research Article

Classification and Analysis of Soil Types using Bayesian Models for the Crop Agronomy

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  • @ARTICLE{10.4108/ew.4271,
        author={A. Zakiuddin Ahmed and T. Abdul Razak},
        title={Classification and Analysis of Soil Types using Bayesian Models for the Crop Agronomy},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={EW},
        year={2023},
        month={10},
        keywords={Soil types, Decision tree, Machine learning, Bayesian models, agronomy},
        doi={10.4108/ew.4271}
    }
    
  • A. Zakiuddin Ahmed
    T. Abdul Razak
    Year: 2023
    Classification and Analysis of Soil Types using Bayesian Models for the Crop Agronomy
    EW
    EAI
    DOI: 10.4108/ew.4271
A. Zakiuddin Ahmed1,*, T. Abdul Razak1
  • 1: Bharathidasan University
*Contact email: azaki2020@gmail.com

Abstract

The core objective of this research paper is to classify soil a particular region and also perform the scientific study for the prediction of suitable crops, which will yield more profit to the agronomist.  The production of agriculture is mainly depends on soil type, cultivation seasons (climate), irrigation method (such as surface, sprinkler, drip/trickle, subsurface etc) and fertilizers (to increase the crop productivity).  Soil is classified based on its physical properties (color, texture, structure, porosity, density etc) and chemical properties (phosphorous, nitrogen, carbon, calcium, magnesium, sodium, pH, potassium, sulfur etc). In this research paper soil types are classified by applying Bayesian models to decision tree algorithm and performed various analysis to verify that soil types are correctly classified and as wells to predict suitable crop cultivation during Kharif (monsoon),  Rabi (winter) and Zaid (summer) seasons along with suitable irrigation methods and  proper use of fertilizers to increase the crop productivity. The proposed algorithm offers unique features by adopting other tree inducers which produce optimum results even though the conditions are leads to chaotic behavior.  The final results obtained are presented which illustrates optimum results and generates best decision tree for classification of  soil type from the soil dataset. The Bayesian approach guarantees that the classifications of soil types are more accurate than the existing Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Decision Tree (DT) algorithms.