Prediction Of Soil Texture Using Convolution Neural Network with Enhanced Regression Model

Volume 20 No 7 (2022)

K. Anandan, R. Shankar , S. Duraisamy

DOI: 10.14704/nq.2022.20.7.NQ22033

Abstract

The chemical properties in the soil are most important factors to predict the texture of soil and it helps to decide about the farming accordingly. There are several chemical properties are present in different region and only few taken for prediction of texture in the proposed research. The proposed research has taken only eight parameters from the soil to predict the texture such as organic carbon, cation exchange capacity, nitrogen, pH level, clay, sand, potassium and phosphorous

Keywords

Soil texture; chemical properties; convolution neural network; linear regression algorithm; mean square error; deep learning

Copyright © Neuroquantology

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Articles published in the Neuroquantology are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJECSE right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.