The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • GIDP 2026
  • ICONS_BA 2025

Computer Vision Conference (CVC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • RSS Feed

DOI: 10.14569/IJACSA.2023.0141111
PDF

Investigating Efficiency of Soil Classification System using Neural Network Models

Author 1: Pappala Mohan Rao
Author 2: Kunjam Nageswara Rao
Author 3: Sitaratnam Gokuruboyina
Author 4: Neeli Koti Siva Sai Priyanka

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 11, 2023.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Soil is a vital requirement for agricultural activities providing numerous functionalities restoring both abiotic and biotic materials. There are different types of soils, and each type of soil possesses distinctive characteristics and unique harvesting properties that impact agricultural development in various ways. Generally, farmers in the olden days used to analyse soil by looking at it visually while some prefer laboratory tests which are time-consuming and costly. Testing of soil is done to analyse the features and characteristics of the soil type, which results in selecting a suitable crop. This in turn results in increased food productivity which is very beneficial to farmers. Hence, to recognize the soil type an automatic soil identification model is proposed by implementing Deep Learning Techniques. It is used to classify the soil for crop recommendation by analysing accurate soil type. Different Convolution Neural Networks have been applied in the proposed model. They are VGG16, VGG19, InceptionV3 and ResNet50.Among all those techniques it is analysed that better results were obtained with ResNet50 having an accuracy of about 87% performing Multi-classification that is Black soil, Laterite Soil, Yellow Soil, Cinder soil & Peat soil.

Keywords: Agricultural; convolution neural network; soil classification deep learning; VGG16; VGG19; InceptionV3; multi-classification; ResNet50

Pappala Mohan Rao, Kunjam Nageswara Rao, Sitaratnam Gokuruboyina and Neeli Koti Siva Sai Priyanka. “Investigating Efficiency of Soil Classification System using Neural Network Models”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.11 (2023). http://dx.doi.org/10.14569/IJACSA.2023.0141111

@article{Rao2023,
title = {Investigating Efficiency of Soil Classification System using Neural Network Models},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0141111},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0141111},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {11},
author = {Pappala Mohan Rao and Kunjam Nageswara Rao and Sitaratnam Gokuruboyina and Neeli Koti Siva Sai Priyanka}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

Computer Vision Conference (CVC) 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

Artificial Intelligence Conference 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Future Technologies Conference (FTC) 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computer Vision Conference
  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

The Science and Information (SAI) Organization Limited is a company registered in England and Wales under Company Number 8933205.