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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Veröffentlicht in:IEEE sensors journal Jg. 21; H. 16; S. 17479 - 17491
Hauptverfasser: Kashyap, Pankaj Kumar, Kumar, Sushil, Jaiswal, Ankita, Prasad, Mukesh, Gandomi, Amir H.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 15.08.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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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Zusammenfassung: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.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2021.3069266