ICTACT Journal on Soft Computing

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Managing Irrigation Needs Based On Smart Decisions Using Machine Learning


Affiliations
1 Department of Computer Science and Engineering, Government College of Technology, Coimbatore, India
     

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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

Keywords

Classification Algorithms, Decision Support Systems, Internet of Things, Machine learning Algorithms, Smart Irrigation
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  • Managing Irrigation Needs Based On Smart Decisions Using Machine Learning

Abstract Views: 246  |  PDF Views: 1

Authors

R. Bhavani
Department of Computer Science and Engineering, Government College of Technology, Coimbatore, India
Ajoe Thambi
Department of Computer Science and Engineering, Government College of Technology, Coimbatore, India

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

Keywords


Classification Algorithms, Decision Support Systems, Internet of Things, Machine learning Algorithms, Smart Irrigation

References