Application of an IoT and Machine Learning Smart Irrigation System to Minimize Water Usage Within the Agriculture Sector

J. N. Kaggwa; A. Telukdarie

doi:10.1109/IEEM58616.2023.10406446

Application of an IoT and Machine Learning Smart Irrigation System to Minimize Water Usage Within the Agriculture Sector

J. N. Kaggwa
, A. Telukdarie

University of Johannesburg

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

Globally, farmers are faced with the dilemma of supplying optimal water for crops amidst the ever-increasing extreme weather conditions. Optimal water supply to crops has both cost and crop productivity implications for farmers. New technological advancements have led towards the developing of smart irrigation systems which ensure the efficient consumption of water during irrigation, mainly by applying Internet of Things (IoT). This research considers three crop types namely, beans, chilli and potato, and their respective threshold soil moisture content values. The results show that when beans, chilli and potato were selected, the system issued a command to irrigate for soil moisture values below the threshold soil moisture content, and not irrigate for values above the threshold moisture content, respectively. Moreover, the use of machine learning will enable the system to reduce the need and the cost for extensive sensor network infrastructure, thereby improving on cost efficiencies reported on smart irrigation systems that incorporate IoT technology.

Original language	English
Title of host publication	2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1672-1676
Number of pages	5
ISBN (Electronic)	9798350323153
DOIs	https://doi.org/10.1109/IEEM58616.2023.10406446
Publication status	Published - 2023
Externally published	Yes
Event	2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023 - Singapore, Singapore Duration: 18 Dec 2023 → 21 Dec 2023

Publication series

Name	2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023

Conference

Conference	2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023
Country/Territory	Singapore
City	Singapore
Period	18/12/23 → 21/12/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 6 Clean Water and Sanitation
SDG 8 Decent Work and Economic Growth

Keywords

Internet of Things (IoT)
Machine Learning
Smart Irrigation
Soil Moisture

ASJC Scopus subject areas

Artificial Intelligence
Decision Sciences (miscellaneous)
Statistics, Probability and Uncertainty
Industrial and Manufacturing Engineering
Modeling and Simulation
Strategy and Management

Access to Document

10.1109/IEEM58616.2023.10406446

Cite this

Kaggwa, J. N., & Telukdarie, A. (2023). Application of an IoT and Machine Learning Smart Irrigation System to Minimize Water Usage Within the Agriculture Sector. In 2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023 (pp. 1672-1676). (2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IEEM58616.2023.10406446

Kaggwa, J. N. ; Telukdarie, A. / Application of an IoT and Machine Learning Smart Irrigation System to Minimize Water Usage Within the Agriculture Sector. 2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 1672-1676 (2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023).

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abstract = "Globally, farmers are faced with the dilemma of supplying optimal water for crops amidst the ever-increasing extreme weather conditions. Optimal water supply to crops has both cost and crop productivity implications for farmers. New technological advancements have led towards the developing of smart irrigation systems which ensure the efficient consumption of water during irrigation, mainly by applying Internet of Things (IoT). This research considers three crop types namely, beans, chilli and potato, and their respective threshold soil moisture content values. The results show that when beans, chilli and potato were selected, the system issued a command to irrigate for soil moisture values below the threshold soil moisture content, and not irrigate for values above the threshold moisture content, respectively. Moreover, the use of machine learning will enable the system to reduce the need and the cost for extensive sensor network infrastructure, thereby improving on cost efficiencies reported on smart irrigation systems that incorporate IoT technology.",

keywords = "Internet of Things (IoT), Machine Learning, Smart Irrigation, Soil Moisture",

author = "Kaggwa, \{J. N.\} and A. Telukdarie",

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year = "2023",

doi = "10.1109/IEEM58616.2023.10406446",

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series = "2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023",

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Kaggwa, JN & Telukdarie, A 2023, Application of an IoT and Machine Learning Smart Irrigation System to Minimize Water Usage Within the Agriculture Sector. in 2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023. 2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023, Institute of Electrical and Electronics Engineers Inc., pp. 1672-1676, 2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023, Singapore, Singapore, 18/12/23. https://doi.org/10.1109/IEEM58616.2023.10406446

Application of an IoT and Machine Learning Smart Irrigation System to Minimize Water Usage Within the Agriculture Sector. / Kaggwa, J. N.; Telukdarie, A.
2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1672-1676 (2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Application of an IoT and Machine Learning Smart Irrigation System to Minimize Water Usage Within the Agriculture Sector

AU - Kaggwa, J. N.

AU - Telukdarie, A.

PY - 2023

Y1 - 2023

N2 - Globally, farmers are faced with the dilemma of supplying optimal water for crops amidst the ever-increasing extreme weather conditions. Optimal water supply to crops has both cost and crop productivity implications for farmers. New technological advancements have led towards the developing of smart irrigation systems which ensure the efficient consumption of water during irrigation, mainly by applying Internet of Things (IoT). This research considers three crop types namely, beans, chilli and potato, and their respective threshold soil moisture content values. The results show that when beans, chilli and potato were selected, the system issued a command to irrigate for soil moisture values below the threshold soil moisture content, and not irrigate for values above the threshold moisture content, respectively. Moreover, the use of machine learning will enable the system to reduce the need and the cost for extensive sensor network infrastructure, thereby improving on cost efficiencies reported on smart irrigation systems that incorporate IoT technology.

AB - Globally, farmers are faced with the dilemma of supplying optimal water for crops amidst the ever-increasing extreme weather conditions. Optimal water supply to crops has both cost and crop productivity implications for farmers. New technological advancements have led towards the developing of smart irrigation systems which ensure the efficient consumption of water during irrigation, mainly by applying Internet of Things (IoT). This research considers three crop types namely, beans, chilli and potato, and their respective threshold soil moisture content values. The results show that when beans, chilli and potato were selected, the system issued a command to irrigate for soil moisture values below the threshold soil moisture content, and not irrigate for values above the threshold moisture content, respectively. Moreover, the use of machine learning will enable the system to reduce the need and the cost for extensive sensor network infrastructure, thereby improving on cost efficiencies reported on smart irrigation systems that incorporate IoT technology.

KW - Internet of Things (IoT)

KW - Machine Learning

KW - Smart Irrigation

KW - Soil Moisture

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DO - 10.1109/IEEM58616.2023.10406446

M3 - Conference contribution

AN - SCOPUS:85186084457

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SP - 1672

EP - 1676

BT - 2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023

Y2 - 18 December 2023 through 21 December 2023

ER -

Kaggwa JN, Telukdarie A. Application of an IoT and Machine Learning Smart Irrigation System to Minimize Water Usage Within the Agriculture Sector. In 2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1672-1676. (2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023). doi: 10.1109/IEEM58616.2023.10406446

Application of an IoT and Machine Learning Smart Irrigation System to Minimize Water Usage Within the Agriculture Sector

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this