Analysis of soil and prediction of crop yield using machine learning approach

Saurabh Balaji Bhganagare , Aniket Babu Rathod , Komal Ramkrushna Wagh , Neha Sunil Kadam

Soil series, Land type, Chemical feature, Geographical attribute, machine learning, CNN, Regression

There are so many soil series available in India. Every soil series have different characteristics and every soil is suitable for different crop. Sometimes it happens that farmer soil is best for some specific crop but as he don’t know. The main goal of the given work is to create a suitable model for classifying various kinds of soil series data along with suitable crops suggestion. Series are recognized by machine learning methods using various chemical features and possible crops for that soil series are suggested using geographical attributes.

Published Paper   E-Certificate

"Analysis of soil and prediction of crop yield using machine learning approach", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 11, page no.1051 - 1053, November-2022, Available :https://ijsdr.org/papers/IJSDR2211155.pdf

Volume 7 Issue 11, November-2022

Pages : 1051 - 1053

Paper Reg. ID: IJSDR_202703

Published Paper Id: IJSDR2211155

Downloads: 000347185

Research Area: Engineering

Country: Nashik, Maharashtra, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2211155

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2211155

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition