A Review on Soil Property Detection using Machine Learning Approach

Authors

  • Smriti Singhatiya P.G. Student, Department of Computer Science and Engineering, MPCT, Gwalior, India
  • Dr. Shivnath Ghosh Associate Professor, Department of Computer Science and Engineering, MPCT, Gwalior, India

DOI:

https://doi.org/10.24113/ijoscience.v4i8.152

Abstract

The agricultural sector is the backbone of the Indian economy. Although focused on industrialization, agriculture remains an important sector of the Indian economy, both in terms of contribution to gross domestic product (GDP) and jobs for millions of people across the country. One of the key factor for productive agriculture is soil. The purpose of the work is to predict the type of terrain using data mining classification methods. Agricultural properties and soil ownership play a crucial role in agricultural decision-making. This research sought to evaluate various mining association techniques and apply them to a soil database to determine if significant relationships could be created. Performance prediction is one of the applications that uses the concept of data mining to increase crop productivity. This makes the problem of crop productive performance is an interesting challenge. An earlier performance prediction was made taking into account the cultivator's experience with a particular crop and culture. This work introduces a system that uses data mining techniques to predict the category of analyzed soil datasets.

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Published

08/05/2018

How to Cite

Singhatiya, S. ., & Ghosh, D. S. . (2018). A Review on Soil Property Detection using Machine Learning Approach. SMART MOVES JOURNAL IJOSCIENCE, 4(8), 6–9. https://doi.org/10.24113/ijoscience.v4i8.152