Open Access Open Access  Restricted Access Subscription Access

Survey on Improving the Prediction of Soil Classification and Crop Suggestion

Dr K. A. Waghmare, Sheetal A. Jhare

Abstract


Agriculture plays an important role in the Indian economy. It remains the major provider for employees and source of revenue of our country. The main focus of this survey is on how to improve the soil quality and crop production. We are going to study the classification problem and prediction of village wise soil parameters. Both are dependent on soil testing samples for finding soil fertility indices and pH values which represent a detail overviewing on application of machine learning in agriculture base. Mostly above problems are solved using machine learning technique which also achieve better accuracy in these areas. 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.

Full Text:

PDF

References


Dhivya B H, Manjula R, Siva Bharathi S, Manjula R, Madhumathi R. A Survey on Crop Yield Prediction based on Agriculture Data.www.ijirset.com. 2017.6(3).

Juhi Reahma S R K, Anitha S. Pillai. Edaphic factors and crop growth using Machine learning – A Review. International Conference on Intelligent Sustainable System. 2017.22.

M.S. Sirsat, E. Cernadasa, M.Fernandez-Delgadoa,* R.Khan. Classification of agriculture soil parameters in India.https://www.esearchgate.net. April 2017.10.(14)

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. Oct 2018.16.

G. Vishwal, J. Venkatesh, Dr. C.Geetha. Crop Variety Selection Method using Machine Learning. International Journal of Innovation in Engineering and Technology (IJIET).March 2019.12(4).

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.

Subhadra Mishra, Debahuti Mishra and Gour Hari Santra Siksha 'O' Anusandha, “Applications of Machine Learning Techniques in Agricultural Crop Production: A Review Paper”, Indian Journal of Science and Technology. Issue October2016.9(38).

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 2018.29(Aug2018).

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), May, 2018.8.(7).

Mo Zhang, Wenjiao Shi1.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. February2019.11.


Refbacks

  • There are currently no refbacks.