Soil Classification System
Rahul Ankalkoti
, Priya B , Vishwas B , Laxmi G , Prof Chetana R Shivanagi
CNN,ML,SVM,K-Means,Python
Soil classification is one of the major affairs and emanating topics in a large number of countries. The population of the world is rising at a majorly rapid pace and along with the increase in population, the demand for food surges actively. For proper crop yield, farmers should be aware of the correct soil type for a particular crop, which affects the increased demand for food. There are various laboratory and field methods to classify soil, but these have limitations like time and labor-consuming. There is a requirement of computer-based soil classification techniques which will help farmers in the field and won't take a lot of time. Here we talk about different computer-based soil classification practices divided into two streams. First is image processing and computer vision-based soil classification approaches which include the conventional image processing algorithms and methods to classify soil using different features like texture, color, and particle size. Second is deep learning and machine learning- based soil classification approaches.
"Soil Classification System", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 5, page no.727 - 732, May-2024, Available :https://ijsdr.org/papers/IJSDR2405099.pdf
Volume 9
Issue 5,
May-2024
Pages : 727 - 732
Paper Reg. ID: IJSDR_211343
Published Paper Id: IJSDR2405099
Downloads: 000347682
Research Area: Engineering
Country: Bagalkot, Karnataka, India
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
