A smart agriculture IoT system based on deep reinforcement learning
Smart agriculture systems based on Internet of Things are the most promising to increase food production and reduce the consumption of resources like fresh water. In this study, we present a smart agriculture IoT system based on deep reinforcement learning which includes four layers, namely agricult...
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| Published in | Future generation computer systems Vol. 99; pp. 500 - 507 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.10.2019
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Smart agriculture systems based on Internet of Things are the most promising to increase food production and reduce the consumption of resources like fresh water. In this study, we present a smart agriculture IoT system based on deep reinforcement learning which includes four layers, namely agricultural data collection layer, edge computing layer, agricultural data transmission layer, and cloud computing layer. The presented system integrates some advanced information techniques, especially artificial intelligence and cloud computing, with agricultural production to increase food production. Specially, the most advanced artificial intelligence model, deep reinforcement learning is combined in the cloud layer to make immediate smart decisions such as determining the amount of water needed to be irrigated for improving crop growth environment. We present several representative deep reinforcement learning models with their broad applications. Finally, we talk about the open challenges and the potential applications of deep reinforcement learning in smart agriculture IoT systems.
•We design a smart agriculture IoT system based on an edge-cloud computing.•We present several representative deep reinforcement learning models.•We discuss the possible challenges and applications of deep reinforcement learning in smart agriculture. |
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| AbstractList | Smart agriculture systems based on Internet of Things are the most promising to increase food production and reduce the consumption of resources like fresh water. In this study, we present a smart agriculture IoT system based on deep reinforcement learning which includes four layers, namely agricultural data collection layer, edge computing layer, agricultural data transmission layer, and cloud computing layer. The presented system integrates some advanced information techniques, especially artificial intelligence and cloud computing, with agricultural production to increase food production. Specially, the most advanced artificial intelligence model, deep reinforcement learning is combined in the cloud layer to make immediate smart decisions such as determining the amount of water needed to be irrigated for improving crop growth environment. We present several representative deep reinforcement learning models with their broad applications. Finally, we talk about the open challenges and the potential applications of deep reinforcement learning in smart agriculture IoT systems.
•We design a smart agriculture IoT system based on an edge-cloud computing.•We present several representative deep reinforcement learning models.•We discuss the possible challenges and applications of deep reinforcement learning in smart agriculture. |
| Author | Wang, Xin Bu, Fanyu |
| Author_xml | – sequence: 1 givenname: Fanyu surname: Bu fullname: Bu, Fanyu email: bufanyu@imufe.edu.cn organization: College of Computer and Information Management, Inner Mongolia University of Finance and Economics, Hohhot, China – sequence: 2 givenname: Xin surname: Wang fullname: Wang, Xin organization: Center of Information and Network Technology, Inner Mongolia Agricultural University, Hohhot, China |
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| SubjectTerms | Cloud computing Deep reinforcement learning Edge computing Smart agriculture IoT |
| Title | A smart agriculture IoT system based on deep reinforcement learning |
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