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Digital Soil Mapping Using Machine Learning

EasyChair Preprint no. 8032

8 pagesDate: May 23, 2022

Abstract

Agriculture is a non-technical sector where in technology can be incorporated for the betterment. Soil analysis is a method to analyse the available plant nutrients in the soil. Soil provides major nutrients to the plants. To create a prediction engine for most appropriate crop for a particular soil. It also determines the type of soil and its fertility. This work predicts the suitable crop and the fertility of a particular soil by analyzing the major and micro nutrients present in the soil. There are mainly three soil parameters that come into consideration when we have to predict the quality of the soil. This method suggests the soil fertility and suitable crop for a soil using Machine Learning Techniques.Our result gives the compatible crop for a particular soil sample by considering important soil parameters and by applying appropriate Machine Learning algorithm. Suitable crop for a particular sample is predicted on the basis of NPK factor, type of soil according to the pH level and soil fertility on the basis of major and micro nutrients with the maximum accuracy.

Keyphrases: Accuracy, machine learning, Nutrients parameters., Soil prediction, soil testing

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:8032,
  author = {Ashu Bansal and Riya Jain and Muskan Rastogi and Shishir Rastogi},
  title = {Digital Soil Mapping Using Machine Learning},
  howpublished = {EasyChair Preprint no. 8032},

  year = {EasyChair, 2022}}
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