A Review : Improving the Village wise Soil Parameter and Predict the Crop Suggestion

Authors

  • Dr. K. A. Waghmare  Department of CSE, Government College of Engineering, Amravati, India
  • Sheetal A. Jhare   Department of CSE, Government College of Engineering, Amravati, India

DOI:

https://doi.org/10.32628/CSEIT2062171

Keywords:

Machine Learning techniques, Soil parameters, Xgboost, logistical regression Classification problem, Crop prediction.

Abstract

India economy majorly depend on agriculture that play important role in the survival of the people. It's remain the major provider for farmers and source of revenue of our country. The main focus of this survey is on how to improve the soil quality and predict the Crop selection. We are going to study the Edaphic factor, Classification problem and prediction of village wise soil parameters. That is done by collecting number of soil testing samples for finding soil fertility indices and pH values which represent a detail overview on application of machine learning in agriculture base . Mostly above problem are solved using two advance classifier Xgboost and Logistical regression which also achieve better accuracy in these area. By applying machine learning in real time data which enabled program to present high testimonial and deep perceptivity for experts and farmers to make correct decision and take proper action

References

  1. Dhivya B H, Manjula R, Siva Bharathi S3Manjula R, Madhumathi R, “ A Survey on Crop Yield Prediction based on Agriculture Data”, www.ijirset.com Vol. 6, Issue 3, March 2017.
  2. Juhi Reahma S R K, Anitha S. Pillai,” Edaphic factors and crop growth using Machine learning – A Review”, International Conference on Intelligent Sustainable System, Vol.22,Feb 2017.
  3. M.S. Sirsat , E. Cernadasa, M.Fernandez-Delgadoa, , R.Khan,” Classification of agriculture soil parameters in India “, https://www.esearchgate.net, Vol.10, Issue 14, April 2017.
  4. M.S. Sirsat, E. Cernadas, M. Fernanadez -Delgado, S. Barro,” Automatic prediction of village-wise soil fertility for several nutrients in India using a wide range of regression method”:http://www.researchgate.net, Issue 16, Oct 2018.
  5. G. Vishwal , J. Venkatesh , Dr. C.Geetha3,” Crop Variety Selection Method using Machine Learning”, International Journal of Innovation in Engineering and Technology(IJIET,) Volume 12, Issue 4 March 2019.
  6. Jay Gholap, Anurag Ingole, Jayesh Gohil, Shailesh Gargade, Vahida Attar ,” Soil Data Analysis Using Classification Techniques and Soil Attribute Prediction”, IEEE International Conference, Issue 2017.
  7. Subhadra Mishra1 , Debahuti Mishra and Gour Hari SantraSiksha 'O' Anusandha,” Applications of Machine Learning Techniques in Agricultural Crop Production: A Review Paper”, Indian Journal of Science and Technology, Vol. 9(38), Issue October 2016.
  8. Jacob Kaingo , Siza D. Tumbo,Nganga I. Kihupi,and Boniface P. Mbilinyi DEST,” Prediction of Soil Moisture-Holding Capacity with Support Vector Machines in Dry Subhumid Tropics”, Applied and Environmental Soil Science Volume 2018, Issue 29 Aug 2018.
  9. S.R.Rajeswari , Parth Khunteta, Subham Kumar,Amrit Raj Singh,Vaibhav Pandey.” Smart Farming Prediction Using Machine Learning”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) , Volume-8 Issue-7 May, 2018.
  10. Mo Zhang1,2, Wenjiao Shi1,3,” Systematic comparison of five machine-learning methods in classification and interpolation of soil particle size fractions using different transformed data”, https://doi.org/10.5194/hess-2018-584 Manuscript under review for journal Hydrol. Earth Syst. Sci. Issue: 11 February 2019.
  11. Kodimalar Palanivel, Chellammal Surianarayanan,” An Approach for Prediction of Crop Yield Using Machine Learning and Big Data Techniques”, International Journal of Computer Engineering and Technology (IJCET) Volume 10, Issue 03, May-June 2019.
  12. El-Sayed Ewis Omran,” Improving the Prediction Auccuracy of Soil Mapping through Geostatistics”, International Journal of Geosciences, Vol-3, Issue- July 2012.
  13. Mareike Ließ, Johannes Schmidt , Bruno Glaser4 ,” Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches”, s. PLoS ONE 11(4): e0153673. doi:10.1371/journal.pone.0153673, Issue: April 29, 2016.
  14. S.Kodur,” Improving the prediction of soil evaporation for different soil types under dryland cropping”, journal home page: www.elsevier.com/locate/agwat, Issue 24 July 2017.
  15. S.Pudumalar,E.Ramanujam, R.Harine Rajashreen, C.Kavyań, T.Kiruthikań, J.Nishań,” Crop Recommendation System for Precision Agriculture”,IEEE Eighth International Conference on Advanced Computing (ICoAC), Issue-2016.
  16. Prof. D.S. Zingade1 ,Omkar Buchade2 ,Nilesh Mehta ,Shubham Ghodekar4 ,Chandan Mehta5,” Crop Prediction System using Machine Learning”, International Journal of Advance Engineering and Research Development Special, Issue on Volume 4, Special Issue 5, Dec.-2017.
  17. Konstantinos G. Liakos , Patrizia Busato , Dimitrios Moshou, Simon Pearson 4 ID and Dionysis Bochtis,” Machine Learning in Agriculture: A Review”, www.mdpi.com,Vol-18, 2018.
  18. Meihua Yang, Dongyun Xu , Songchao Chen , Hongyi Li and Zhou Shi ,” Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using vis-NIR Spectra”,www.mdpi.com,Issue : 11 January 2019.
  19. Vrushal Milan Dolas et al,” A Novel Approach for Classification of Soil and Crop Prediction” International Journal of Computer Science and Mobile Computing, Vol.7 Issue.3, March- 2018.
  20. Sk Al Zaminur Rahman Kaushik Chandra Mitra S.M. Mohidul Islam,” Soil Classification using Machine Learning Methods and Crop Suggestion Based on Soil Series”, International Conference of Computer and Information Technology (ICCIT), Issue-December, 2018.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2020-05-30

Issue

Volume 6, Issue 3, May-June-2020

Section

Research Articles

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How to Cite

[1]
Dr. K. A. Waghmare, Sheetal A. Jhare , " A Review : Improving the Village wise Soil Parameter and Predict the Crop Suggestion " International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 3, pp.120-125, May-June-2020. Available at doi : https://doi.org/10.32628/CSEIT2062171