Classification and Analysis of Soil Types using Bayesian Models for the Crop Agronomy
Keywords:Soil types, Decision tree, Machine learning, Bayesian models, agronomy
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.
Amir Ahmad and Gavin Brown “Random Projection Random Discretization Ensembles—Ensembles of Linear Multivariate Decision Trees”, IEEE Transactions on Knowledge and data Engineering vol.26, No.5, pp.1225-1239May2014, ISSN:1041 – 4347.
D. Anantha Reddy, B. Dadore, and A. Watekar, Crop recommendation system to maximize crop yield in Ramtek region using machine learning, International Journal of Scientific Research in Science and Technology 6 (2019), pp. 485–489. doi:10.32628/IJSRST196172.
Anju Rathee, Robin Prakash Mathur, “Survey on Decision Tree Classification Algorithms for the Evaluation of Student Performance”, International Journal of Computers and Technology (IJCST), Vol. 2 No. 4, pp.244-247, April 2014, ISSN:2277-3061.
P.Barghavi and S.Jyothi, “Applying Naïve Bayes Data Mining Technique for Classification of Agricultural Land Soils,” International Journal of Computer Science and Network Security, Vol.9, No.8, pp.117-122, August2013.
R. Jahan, Applying Naive Bayes classification technique for classification of improved agricultural land soils, International Journal for Research in Applied Science & EngineeringTechnology (IJRASET) 6 (2018), pp. 189–193. doi:10.22214/ijraset.2018.5030.
Nadimani G.V, Hemalatha. M, “An Evaluation of Clustering Technique over Intrusion Detection System”, International Conference on Advances in Computing, Communication and Informatics (ICACCI), pp. 1054-1060, August 2012.
Rahman S.A.Z., Mitra, K.C., Islam, S.M.M, Soil classification using machine learning methods and crop suggestion based on soil series. In: International Conference of Science and Technology Computer (2018)
Rajeswari.V and Arunesh P.K, “Analysis of Soil data using data mining Classification Techniques”, Indian Journal of Science and Technology, Vol.9(19), pp.1-4, May2016, ISSN:0974-6846
R. Vamanan and K. Ramar, “Classification of agricultural land soils a data mining approach,” International Journal of Computer Science and Engineering, Vol.3, No.1,pp.379-384,January2016,ISSN:0975-3397.
Train, K. E. Discrete Choice Models with Simulation. Cambridge University Press, Cambridge, 2009.
Vogel, S.; Ney, H.; Tillmann C, HMM-Based Word Alignment in Statistical Translation. Available online: http://aclweb.org/anthology/C96-2141
Yoon, B.J. Hidden Markov Models and their Applications in Biological Sequence Analysis. Curr. Genom, 2009, 10, 402–415.
N.G.Yethiraj, “Applying Data Mining Techniques in the field of agriculture and allied sciences”, International Journal of Business Intelligent, Vol.01, Issue2, pp72-76, December 2012, ISSN 2278 – 2400.
How to Cite
Copyright (c) 2023 EAI Endorsed Transactions on Energy Web
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.