Authors :
Puneeth M K, Mysore; Ranjit K N; Hemanth; Abhishek B V.; Dharma Rakshith M V.
Volume/Issue :
Volume 9 - 2024, Issue 5 - May
Google Scholar :
https://tinyurl.com/ycxhdysa
Scribd :
https://tinyurl.com/2bup8h34
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAY040
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
This paper proposes a comprehensive
approach to smart irrigation by integrating intrusion
detection mechanisms. By combining the functionalities
of smart irrigation systems with intrusion detection
systems (IDS), the proposed framework offers enhanced
security and reliability in agricultural water
management. The system employs a network of sensors
weather, and soil moisture levels patterns, and other
relevant parameters to optimize irrigation scheduling.
Concurrently, it utilizes intrusion detection algorithms
to identify and respond to unauthorized access attempts
or anomalous behaviors within the irrigation
infrastructure. The proposed approach represents a
noteworthy advancement in the direction of sustainable
and secure management of water in agriculture,
contributing to improved crop yields, resource
conservation, and overall perseverance in the face of
emerging challenges. Additionally, by incorporating
machine learning algorithms into the intrusion
detection system, the framework is able to change and
grow over time, enhancing its capacity to identify and
neutralize possible threats. Furthermore, the system can
more accurately predict when irrigation is needed by
utilizing real-time data analysis and predictive modeling
approaches. This maximizes water usage efficiency
while reducing waste. This all-encompassing strategy
encourages a more ecologically sustainable method of
managing water resources while also strengthening the
resilience of farming operations. Furthermore, the
framework enables farmers to take educated decisions
in real-time, maximizing productivity and lowering
risks, by giving them actionable insights and alerts
about security breaches and irrigation needs.
Keywords :
Agricultural Water Management, Sensor Networks, Irrigation Scheduling Optimization, Resource Conservation, Sustainable Agriculture, Resilience.
References :
- Raza, M. et al. (2020). Internet of Things (IoT)-Based Smart Irrigation Systems: A Review. Sensors, 20(3), 840. DOI: 10.3390/s20030840.
- J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68-73.
- I.S. Jacobs and C.P. Bean, “Fine particles, thin films and exchange anisotropy,” in Magnetism, vol. III, G.T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271-350.
- K. Elissa, “Title of paper if known,” unpublished.
- R. Nicole, “Title of paper with only first word capitalized,” J. Name Stand. Abbrev., in press.
- Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interface,” IEEE Transl. J. Magn. Japan, vol. 2, pp. 740-741, August 1987 [Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982].
- M. Young, The Technical Writer’s Handbook. Mill Valley, CA: University Science, 1989.
This paper proposes a comprehensive
approach to smart irrigation by integrating intrusion
detection mechanisms. By combining the functionalities
of smart irrigation systems with intrusion detection
systems (IDS), the proposed framework offers enhanced
security and reliability in agricultural water
management. The system employs a network of sensors
weather, and soil moisture levels patterns, and other
relevant parameters to optimize irrigation scheduling.
Concurrently, it utilizes intrusion detection algorithms
to identify and respond to unauthorized access attempts
or anomalous behaviors within the irrigation
infrastructure. The proposed approach represents a
noteworthy advancement in the direction of sustainable
and secure management of water in agriculture,
contributing to improved crop yields, resource
conservation, and overall perseverance in the face of
emerging challenges. Additionally, by incorporating
machine learning algorithms into the intrusion
detection system, the framework is able to change and
grow over time, enhancing its capacity to identify and
neutralize possible threats. Furthermore, the system can
more accurately predict when irrigation is needed by
utilizing real-time data analysis and predictive modeling
approaches. This maximizes water usage efficiency
while reducing waste. This all-encompassing strategy
encourages a more ecologically sustainable method of
managing water resources while also strengthening the
resilience of farming operations. Furthermore, the
framework enables farmers to take educated decisions
in real-time, maximizing productivity and lowering
risks, by giving them actionable insights and alerts
about security breaches and irrigation needs.
Keywords :
Agricultural Water Management, Sensor Networks, Irrigation Scheduling Optimization, Resource Conservation, Sustainable Agriculture, Resilience.