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An experimental comparison of IoT-based and traditional irrigation scheduling on a flood-irrigated subtropical lemon farm

An experimental comparison of IoT-based and traditional irrigation scheduling on a flood-irrigated subtropical lemon farm
An experimental comparison of IoT-based and traditional irrigation scheduling on a flood-irrigated subtropical lemon farm
Over recent years, the demand for supplies of freshwater is escalating with the increasing food demand of a fast-growing population. The agriculture sector of Pakistan contributes to 26% of its GDP and employs 43% of the entire labor force. However, the currently used traditional farming methods such as flood irrigation and rotating water allocation system (Warabandi) results in excess and untimely water usage, as well as low crop yield. Internet of things (IoT) solutions based on real-time farm sensor data and intelligent decision support systems have led to many smart farming solutions, thus improving water utilization. The objective of this study was to compare and optimize water usage in a 2-acre lemon farm test site in Gadap, Karachi, for a 9-month duration, by deploying an indigenously developed IoT device and an agriculture-based decision support system (DSS). The sensor data are wirelessly collected over the cloud and a mobile application, as well as a web-based information visualization, and a DSS system makes irrigation recommendations. The DSS system is based on weather data (temperature and humidity), real time in situ sensor data from the IoT device deployed in the farm, and crop data (Kc and crop type). These data are supplied to the Penman–Monteith and crop coefficient model to make recommendations for irrigation schedules in the test site. The results show impressive water savings (~50%) combined with increased yield (35%) when compared with water usage and crop yields in a neighboring 2-acre lemon farm where traditional irrigation scheduling was employed and where harsh conditions sometimes resulted in temperatures in excess of 50 °C.
Internet of things, Monteith equation, Penman, crop coefficient, decision support system, smart irrigation
1424-8220
4175
Zia, Huma
74118b4c-35ab-44e8-a44f-daa4cc6f83e8
Rehman, Ahsan
e309d809-a5da-4e1c-b8aa-ae02c2597a3d
Harris, Nick R.
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Fatima, Sundus
fc1e04ed-402f-4619-a32a-0946d6d8125a
Khurram, Muhammad
a2d15ef0-85a8-4975-b66f-5129650e3aa6
Harris, Nicholas
237cfdbd-86e4-4025-869c-c85136f14dfd
Zia, Huma
74118b4c-35ab-44e8-a44f-daa4cc6f83e8
Rehman, Ahsan
e309d809-a5da-4e1c-b8aa-ae02c2597a3d
Harris, Nick R.
7c60dfc4-064e-4169-9517-9772edffc992
Fatima, Sundus
fc1e04ed-402f-4619-a32a-0946d6d8125a
Khurram, Muhammad
a2d15ef0-85a8-4975-b66f-5129650e3aa6
Harris, Nicholas
237cfdbd-86e4-4025-869c-c85136f14dfd

Zia, Huma, Rehman, Ahsan, Harris, Nick R., Fatima, Sundus, Khurram, Muhammad and Harris, Nicholas (2021) An experimental comparison of IoT-based and traditional irrigation scheduling on a flood-irrigated subtropical lemon farm. Sensors, 21 (12), 4175. (doi:10.3390/S21124175).

Record type: Article

Abstract

Over recent years, the demand for supplies of freshwater is escalating with the increasing food demand of a fast-growing population. The agriculture sector of Pakistan contributes to 26% of its GDP and employs 43% of the entire labor force. However, the currently used traditional farming methods such as flood irrigation and rotating water allocation system (Warabandi) results in excess and untimely water usage, as well as low crop yield. Internet of things (IoT) solutions based on real-time farm sensor data and intelligent decision support systems have led to many smart farming solutions, thus improving water utilization. The objective of this study was to compare and optimize water usage in a 2-acre lemon farm test site in Gadap, Karachi, for a 9-month duration, by deploying an indigenously developed IoT device and an agriculture-based decision support system (DSS). The sensor data are wirelessly collected over the cloud and a mobile application, as well as a web-based information visualization, and a DSS system makes irrigation recommendations. The DSS system is based on weather data (temperature and humidity), real time in situ sensor data from the IoT device deployed in the farm, and crop data (Kc and crop type). These data are supplied to the Penman–Monteith and crop coefficient model to make recommendations for irrigation schedules in the test site. The results show impressive water savings (~50%) combined with increased yield (35%) when compared with water usage and crop yields in a neighboring 2-acre lemon farm where traditional irrigation scheduling was employed and where harsh conditions sometimes resulted in temperatures in excess of 50 °C.

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Accepted/In Press date: 15 June 2021
Published date: 17 June 2021
Additional Information: Funding Information: This work was sponsored by the Office of Research and Sponsored Programs of Abu Dhabi University. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Keywords: Internet of things, Monteith equation, Penman, crop coefficient, decision support system, smart irrigation

Identifiers

Local EPrints ID: 449966
URI: http://eprints.soton.ac.uk/id/eprint/449966
ISSN: 1424-8220
PURE UUID: 12ad88a3-be26-400e-9a00-8f024dab17b1
ORCID for Nicholas Harris: ORCID iD orcid.org/0000-0003-4122-2219

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Date deposited: 30 Jun 2021 16:31
Last modified: 01 Jul 2021 01:34

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Contributors

Author: Huma Zia
Author: Ahsan Rehman
Author: Nick R. Harris
Author: Sundus Fatima
Author: Muhammad Khurram
Author: Nicholas Harris ORCID iD

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