A smart agriculture IoT system based on deep reinforcement learning - Bu, Fanyu; Wang, Xin (Future generation computer systems, 2019)
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.