Identifying best suited soil based on its physical and chemical properties using machine learning

Name: Nanwal, Jyoti

Organization : J. C. Bose University of Science and Technology. CE Department

Email : nanwaljyoti20@gmail.com

Name: Sethi, Preeti

Organization : J. C. Bose University of Science and Technology. CE Department

Email : preetisethi22@gmail.com

LCSH: Soils -- Classification

LCSH: Soil science

LCSH: Machine learning

Abstract: Soil is the basic element to the life of any living species on the earth. Almost 41% of Indians are working in the agricultural sector and contributing around 19% to Indian GDP. Like any other sector, here also researchers or scientists are trying hard to improve the methods of agriculture by implementing new techniques of Machine Learning, AI, Big Data, etc... In this paper, agricultural sensors are used to get values for the physical and chemical parameters of the soil found in Haryana and these values are used to support vector machine (SVM) and artificial neural network(ANN) to classify our soil samples. After applying these two techniques to our soil samples, analysis reveals that ANN over-performs SVM for classifying the soil based on its physical and chemical properties

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2021

Modified: 2024-06-11

Issued: 2024-05-31

บทความ/Article

application/pdf

BibliograpyCitation : In IEEE Computer Society. 2021 Fourth International Conference on Computational Intelligence and Communication Technologies (CCICT 2021) (pp.35-38). Los Alamitos, CA : IEEE Computer Society

eng

©copyrights King Mongkut's University of Technology North Bangkok

ลำดับที่.	ชื่อแฟ้มข้อมูล	ขนาดแฟ้มข้อมูล	จำนวนเข้าถึง	วัน-เวลาเข้าถึงล่าสุด
1	CCICT 2021pp.35-38.pdf	759.78 KB	1	2024-08-17 00:35:22

ใช้เวลา

0.030707 วินาที