Towards Precision Agriculture: IoT-Enabled Intelligent Irrigation Systems Using Deep Learning Neural Network

Recently, precision agriculture has gained substantial attention due to the ever-growing world population demands for food and water. Consequently, farmers will need water and arable land to meet this demand. Due to the limited availability of both resources, farmers need a solution that changes the...

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Published in:	IEEE sensors journal Vol. 21; no. 16; pp. 17479 - 17491
Main Authors:	Kashyap, Pankaj Kumar, Kumar, Sushil, Jaiswal, Ankita, Prasad, Mukesh, Gandomi, Amir H.
Format:	Journal Article
Language:	English
Published:	New York IEEE 15.08.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Agriculture Arable land Data models Deep learning Farmers Intelligent sensors Internet of Things Irrigation Irrigation systems long short term memory Machine learning Moisture content Neural networks precision agriculture Predictive models sensor Sensors Soil moisture Spatial distribution
ISSN:	1530-437X, 1558-1748
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Abstract	Recently, precision agriculture has gained substantial attention due to the ever-growing world population demands for food and water. Consequently, farmers will need water and arable land to meet this demand. Due to the limited availability of both resources, farmers need a solution that changes the way they operate. Precision irrigation is the solution to deliver bigger, better, and more profitable yields with fewer resources. Several machine learning-based irrigation models have been proposed to use water more efficiently. Due to the limited learning ability of these models, they are not well suited to unpredictable climates. In this context, this paper proposes a deep learning neural network-based Internet of Things (IoT)-enabled intelligent irrigation system for precision agriculture (DLiSA). This is a feedback integrated system that keeps its functionality better in the weather of any region for any period of time. DLiSA utilizes a long short-term memory network (LSTM) to predict the volumetric soil moisture content for one day ahead, irrigation period, and spatial distribution of water required to feed the arable land. It is evident from the simulation results that DLiSA uses water more wisely than state-of-the-art models in the experimental farming area.
AbstractList	Recently, precision agriculture has gained substantial attention due to the ever-growing world population demands for food and water. Consequently, farmers will need water and arable land to meet this demand. Due to the limited availability of both resources, farmers need a solution that changes the way they operate. Precision irrigation is the solution to deliver bigger, better, and more profitable yields with fewer resources. Several machine learning-based irrigation models have been proposed to use water more efficiently. Due to the limited learning ability of these models, they are not well suited to unpredictable climates. In this context, this paper proposes a deep learning neural network-based Internet of Things (IoT)-enabled intelligent irrigation system for precision agriculture (DLiSA). This is a feedback integrated system that keeps its functionality better in the weather of any region for any period of time. DLiSA utilizes a long short-term memory network (LSTM) to predict the volumetric soil moisture content for one day ahead, irrigation period, and spatial distribution of water required to feed the arable land. It is evident from the simulation results that DLiSA uses water more wisely than state-of-the-art models in the experimental farming area.
Author	Gandomi, Amir H. Jaiswal, Ankita Prasad, Mukesh Kashyap, Pankaj Kumar Kumar, Sushil
Author_xml	– sequence: 1 givenname: Pankaj Kumar orcidid: 0000-0002-6041-9327 surname: Kashyap fullname: Kashyap, Pankaj Kumar email: pankaj76_scs@jnu.ac.in organization: School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India – sequence: 2 givenname: Sushil orcidid: 0000-0001-9113-2890 surname: Kumar fullname: Kumar, Sushil email: skdohare@mail.jnu.ac.in organization: School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India – sequence: 3 givenname: Ankita surname: Jaiswal fullname: Jaiswal, Ankita email: ankita79_scs@jnu.ac.in organization: School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India – sequence: 4 givenname: Mukesh surname: Prasad fullname: Prasad, Mukesh email: mukesh.prasad@uts.edu.au organization: Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia – sequence: 5 givenname: Amir H. orcidid: 0000-0002-2798-0104 surname: Gandomi fullname: Gandomi, Amir H. email: gandomi@uts.edu.au organization: Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia
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SubjectTerms	Agriculture Arable land Data models Deep learning Farmers Intelligent sensors Internet of Things Irrigation Irrigation systems long short term memory Machine learning Moisture content Neural networks precision agriculture Predictive models sensor Sensors Soil moisture Spatial distribution
Title	Towards Precision Agriculture: IoT-Enabled Intelligent Irrigation Systems Using Deep Learning Neural Network
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