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A novel autonomous irrigation system for smart agriculture using AI and 6G enabled IoT network.

Authors :
R, Sitharthan
M, Rajesh
S, Vimal
E, Saravana Kumar
S, Yuvaraj
Kumar, Abhishek
I, Jacob Raglend
K, Vengatesan
Source :
Microprocessors & Microsystems. Sep2023, Vol. 101, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

• Agriculture is crucial for human development and depends heavily on irrigation. This paper presents an autonomous irrigation system using AI and 6G-IoT. • The system uses a prediction algorithm based on weather history data to anticipate rainfall patterns and climate changes. • It creates an intelligent system that irrigates the fields according to environmental conditions. • The developed system achieved an accuracy of 86.34%, sensitivity of 89.28%, and precision of 91%. Agriculture plays a vital role in the growth of humankind. The total world depends upon the agricultural contribution, and wherein the agriculture purely depends upon the necessity of irrigation. This paper develops an autonomous irrigation system using Artificial Intelligence (AI) and the 6G enabled Internet of Things (6G-IoT) transforming to create a hassle-free smart agriculture model. The developed autonomous irrigation system is purely based on prediction algorithm which is mount in a microprocessor, which uses weather history data using IoT to identify and predict rainfall patterns and any climatic changes; hence it creates an intelligent system which irrigates the field depending upon the outer environment. The developed system not only checks the environmental condition, but it also measures the moisture content of the soil to supply the required amount of water to the crops. The developed autonomous AI and 6G-IoT based smart agricultural irrigation system has been tested under control environment with an accuracy of 86.34%, sensitivity of 89.28% and precision of 91%. Hence, the proposed methodology could be the possible solution for autonomous irrigation of crops in future. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01419331
Volume :
101
Database :
Academic Search Index
Journal :
Microprocessors & Microsystems
Publication Type :
Academic Journal
Accession number :
170085782
Full Text :
https://doi.org/10.1016/j.micpro.2023.104905