A smart agriculture IoT system based on deep reinforcement learning

Bu, Fanyu; Wang, Xin

doi:10.1016/j.future.2019.04.041

Cited by 264 publications

(87 citation statements)

References 13 publications

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“…The algorithms [130] without any prior knowledge and starting to play random gains, in 24 hours, achieved super-human level performance in chess, shogi as well as Go and convincingly defeated a work-champion program in each case. Since then the algorithm has been applied to solve more engineering problems like advanced planning of autonomous vehicles [131], lung cancer detection in medical treatment [132], smart agriculture [133], UAV cluster task scheduling [134], chatbots [135], autonomous building energy assessment [136].…”

Section: B Data-driven Modelingmentioning

confidence: 99%

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

2020

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A digital twin can be defined as an adaptive model of a complex physical system. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

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Section: B Data-driven Modelingmentioning

confidence: 99%

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

2020

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“…Smart agriculture and smart irrigation are the essentials of tomorrow to compensate the demands for the ever increasing population. This section discusses some of the major and recent contributions in this domain.A deep reinforcement learning model for smart agriculture using IoT was presented by Bu et al [7]. This model is composed of four major layers, the data collection layer, which collects details from the sensors, edge computing layer, which performs minimal computations on the collected data, data transmission layer, which transmits the computed data to the cloud and the cloud computing layer, which performs the major computations.…”

Section: Related Workmentioning

confidence: 99%

Iot and Weather Based Smart Irrigation Monitoring And Controlling System for Agriculture

Amalraj*,

Sivakumar

2019

IJRTE

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Effective and successful agriculture requires effective water management. Irrigation at appropriate periods and at appropriate levels results in profitable yields. Technology can provide an effective solution for this domain. This work presents an IoT based prediction model that can be to create a smart irrigation system for farming. The proposed architecture is composed of three layers; the data collection layer, machine learning based rainfall prediction layer and the rulebased irrigation requirement identification layer. The data collection layer operates in multiple levels using sensors and APIs, obtaining ground based information and also weather information. The machine learning layer performs rainfall prediction based on past data and the final layer uses defined rules to identify the irrigation needs of crops. The major advantage of this model is that it is not fine tuned to a single crop. The model can be used for any crop and can also be used for multiple crops by the same farmer.

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“…Smart agriculture has been capable of offering many solutions to the modernization of agriculture [1]. With the development of Internet of Things (IoT) technology, smart agricultural applications have developed greatly [2][3][4] in recent years.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

Jin

Yang

Wang

et al. 2020

Sensors

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Smart agricultural sensing has enabled great advantages in practical applications recently, making it one of the most important and valuable systems. For outdoor plantation farms, the prediction of climate data, such as temperature, wind speed, and humidity, enables the planning and control of agricultural production to improve the yield and quality of crops. However, it is not easy to accurately predict climate trends because the sensing data are complex, nonlinear, and contain multiple components. This study proposes a hybrid deep learning predictor, in which an empirical mode decomposition (EMD) method is used to decompose the climate data into fixed component groups with different frequency characteristics, then a gated recurrent unit (GRU) network is trained for each group as the sub-predictor, and finally the results from the GRU are added to obtain the prediction result. Experiments based on climate data from an agricultural Internet of Things (IoT) system verify the development of the proposed model. The prediction results show that the proposed predictor can obtain more accurate predictions of temperature, wind speed, and humidity data to meet the needs of precision agricultural production.

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A smart agriculture IoT system based on deep reinforcement learning

Cited by 264 publications

References 13 publications

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

Iot and Weather Based Smart Irrigation Monitoring And Controlling System for Agriculture

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

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