MANAGING IRRIGATION NEEDS BASED ON SMART DECISIONS USING MACHINE LEARNING

Abstract
Optimized utilization of water for agriculture is a big challenge in today’s world. Internet of Things (IoT) based solutions along with machine learning techniques help in achieving effective utilization of waters in farming landspace. This paper presents sensor-based acquisition of soil moisture, temperature and humidity from the farm. Data are then stored in the server and clustered into two groups. Next machine learning based classification models like Naïve Bayes (NB), K-Nearest Neighbor (K-NN) and Support Vector Machines (SVM) are applied to decide irrigation need. The performance measures of the classification models show that K-NN classifier performs better than the other two classification models considered in this study.

Authors
R Bhavani1, Ajoe Thambi2
Government College of Technology, Coimbatore, India1, Government College of Technology, Coimbatore, India2

Keywords
Classification Algorithms, Decision Support Systems, Internet of Things, Machine learning Algorithms, Smart Irrigation
Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 12 , Issue: 2 )
Date of Publication :
January 2022
DOI :

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