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

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.

[1]  Feng Ding,et al.  Maximum Likelihood Recursive Identification for the Multivariate Equation-Error Autoregressive Moving Average Systems Using the Data Filtering , 2019, IEEE Access.

[2]  Yao Wang,et al.  Generalized Recurrent Neural Network accommodating Dynamic Causal Modeling for functional MRI analysis , 2018, NeuroImage.

[3]  Fang-Fang Li,et al.  Hybrid Models Combining EMD/EEMD and ARIMA for Long-Term Streamflow Forecasting , 2018, Water.

[4]  Jie Ding,et al.  Particle filtering based parameter estimation for systems with output-error type model structures , 2019, J. Frankl. Inst..

[5]  Hui Lin,et al.  Towards real-time respiratory motion prediction based on long short-term memory neural networks , 2019, Physics in medicine and biology.

[6]  E. Barkai,et al.  Single-big-jump principle in physical modeling. , 2018, Physical review. E.

[7]  Tingli Su,et al.  Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction , 2019, Applied Sciences.

[8]  Baihai Zhang,et al.  Adaptive filtering for MEMS gyroscope with dynamic noise model. , 2020, ISA transactions.

[9]  A. Ogundipe,et al.  Fiscal Deficit and Economic Growth in Nigeria: Ascertaining a Feasible Threshold , 2016 .

[10]  Laxmidhar Behera,et al.  RNN Based Solar Radiation Forecasting Using Adaptive Learning Rate , 2013, SEMCCO.

[11]  Tao Yu,et al.  An improved empirical mode decomposition method using second generation wavelets interpolation , 2018, Digit. Signal Process..

[12]  Feng Ding,et al.  Gradient-Based Iterative Parameter Estimation Algorithms for Dynamical Systems from Observation Data , 2019, Mathematics.

[13]  Ponnuthurai Nagaratnam Suganthan,et al.  Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting , 2017, Appl. Soft Comput..

[14]  Hiroyuki Tsuji,et al.  A hybrid machine learning approach to automatic plant phenotyping for smart agriculture , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[15]  Zhengquan Xu,et al.  CTS-DP: Publishing correlated time-series data via differential privacy , 2017, Knowl. Based Syst..

[16]  A. S. Elons,et al.  A proposed model for predicting the drilling path based on hybrid Pso-Bp neural network , 2016, 2016 SAI Computing Conference (SAI).

[17]  Tingli Su,et al.  Compound Autoregressive Network for Prediction of Multivariate Time Series , 2019, Complex..

[18]  Prasanna Kumar Sahu,et al.  Denoising of Electrocardiogram (ECG) signal by using empirical mode decomposition (EMD) with non-local mean (NLM) technique , 2018 .

[19]  Durga Toshniwal,et al.  Empirical Mode Decomposition Based Deep Learning for Electricity Demand Forecasting , 2018, IEEE Access.

[20]  Justin Salamon,et al.  Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification , 2016, IEEE Signal Processing Letters.

[21]  Li Quan,et al.  A new service-oriented grid-based method for AIoT application and implementation , 2017 .

[22]  Tingli Su,et al.  Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System , 2020, Sustainability.

[23]  Feng Ding,et al.  State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors , 2019, International Journal of Adaptive Control and Signal Processing.

[24]  Fu Bing.  The research of IOT of agriculture based on three layers architecture , 2016, 2016 2nd International Conference on Cloud Computing and Internet of Things (CCIOT).

[25]  Xiao Zhang,et al.  The innovation algorithms for multivariable state‐space models , 2019, International Journal of Adaptive Control and Signal Processing.

[26]  Qian Sun,et al.  Spatio-Temporal Prediction for the Monitoring-Blind Area of Industrial Atmosphere Based on the Fusion Network , 2019, International journal of environmental research and public health.

[27]  Cem Kocak,et al.  ARMA(p, q) type high order fuzzy time series forecast method based on fuzzy logic relations , 2017, Appl. Soft Comput..

[28]  Xiangkui Wan,et al.  Two-stage Gradient-based Iterative Estimation Methods for Controlled Autoregressive Systems Using the Measurement Data , 2020, International Journal of Control, Automation and Systems.

[29]  Omar Hamdan,et al.  IoT-Based Interactive Dual Mode Smart Home Automation , 2019, 2019 IEEE International Conference on Consumer Electronics (ICCE).

[30]  Tingli Su,et al.  Indoor Tracking by RFID Fusion with IMU Data , 2018, Asian Journal of Control.

[31]  C. Hervás,et al.  Biometeorological and autoregressive indices for predicting olive pollen intensity , 2013, International Journal of Biometeorology.

[32]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[33]  Ahmed Alsaedi,et al.  Gradient estimation algorithms for the parameter identification of bilinear systems using the auxiliary model , 2020, J. Comput. Appl. Math..

[34]  Bao-Hua Zheng.  Material procedure quality forecast based on genetic BP neural network , 2017 .

[35]  Bruce Misstear,et al.  Real time air quality forecasting using integrated parametric and non-parametric regression techniques , 2015 .

[36]  Feng Ding,et al.  Hierarchical Principle-Based Iterative Parameter Estimation Algorithm for Dual-Frequency Signals , 2019, Circuits Syst. Signal Process..

[37]  Jiabin Yu,et al.  An approach of recursive timing deep belief network for algal bloom forecasting , 2018, Neural Computing and Applications.

[38]  Jie Ding,et al.  Particle filtering‐based recursive identification for controlled auto‐regressive systems with quantised output , 2019, IET Control Theory & Applications.

[39]  Jiping Xu,et al.  A novel water quality mechanism modeling and eutrophication risk assessment method of lakes and reservoirs , 2019, Nonlinear Dynamics.

[40]  Marco A. S. Netto,et al.  A Scalable Machine Learning System for Pre-Season Agriculture Yield Forecast , 2018, 2018 IEEE 14th International Conference on e-Science (e-Science).

[41]  Weifeng Chen,et al.  A new method of large-scale short-term forecasting of agricultural commodity prices: illustrated by the case of agricultural markets in Beijing , 2017, Journal of Big Data.

[42]  Jiping Xu,et al.  Hard Decision-Based Cooperative Localization for Wireless Sensor Networks , 2019, Sensors.

[43]  Eissa Alreshidi,et al.  Smart Sustainable Agriculture (SSA) Solution Underpinned by Internet of Things (IoT) and Artificial Intelligence (AI) , 2019, International Journal of Advanced Computer Science and Applications.

[44]  Mohsen Guizani,et al.  Deep Learning for IoT Big Data and Streaming Analytics: A Survey , 2017, IEEE Communications Surveys & Tutorials.

[45]  Taryudi,et al.  Iot-based Integrated Home Security and Monitoring System , 2018, Journal of Physics: Conference Series.

[46]  Tingli Su,et al.  Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction , 2020, Mathematics.

[47]  Feng Ding,et al.  Iterative Parameter Estimation for Signal Models Based on Measured Data , 2018, Circuits Syst. Signal Process..

[48]  Dongxiao Niu,et al.  Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm , 2014 .

[49]  Wei Wei,et al.  Time-Delay System Control Based on an Integration of Active Disturbance Rejection and Modified Twice Optimal Control , 2019, IEEE Access.

[50]  Erfu Yang,et al.  State filtering‐based least squares parameter estimation for bilinear systems using the hierarchical identification principle , 2018, IET Control Theory & Applications.

[51]  Jiping Xu,et al.  An approach of improved Multivariate Timing-Random Deep Belief Net modelling for algal bloom prediction , 2019, Biosystems Engineering.

[52]  F. Ding,et al.  Partially‐coupled least squares based iterative parameter estimation for multi‐variable output‐error‐like autoregressive moving average systems , 2019, IET Control Theory & Applications.

[53]  Fei Shen,et al.  Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks , 2018, IEEE Transactions on Industrial Electronics.

[54]  Yuting Bai,et al.  Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes , 2020, Sustainability.

[55]  Empha Grace Perez,et al.  Malaria Incidence in the Philippines: Prediction using the Autoregressive Moving Average Models. , 2019 .

[56]  Feng Ding,et al.  The filtering‐based maximum likelihood iterative estimation algorithms for a special class of nonlinear systems with autoregressive moving average noise using the hierarchical identification principle , 2019, International Journal of Adaptive Control and Signal Processing.

[57]  Duo Zhang,et al.  Use Long Short-Term Memory to Enhance Internet of Things for Combined Sewer Overflow Monitoring , 2018 .

[58]  Victor I. Chang,et al.  Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare , 2018, Future Gener. Comput. Syst..

[59]  Chee Yen Leow,et al.  An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges , 2018, IEEE Internet of Things Journal.

[60]  Jiabin Yu,et al.  A Hybrid Path Planning Method for an Unmanned Cruise Ship in Water Quality Sampling , 2019, IEEE Access.

[61]  Wang Jun,et al.  A weighted EMD-based prediction model based on TOPSIS and feed forward neural network for noised time series , 2017, Knowl. Based Syst..

[62]  Xanthoula Eirini Pantazi,et al.  Wheat yield prediction using machine learning and advanced sensing techniques , 2016, Comput. Electron. Agric..

[63]  Chunhua Mu,et al.  Research on Key Technologies of Intelligent Agriculture Based on Agricultural Big Data , 2016, 2016 International Conference on Smart City and Systems Engineering (ICSCSE).

[64]  René Ruby-Figueroa,et al.  Permeate flux prediction in the ultrafiltration of fruit juices by ARIMA models , 2017 .

[65]  Baojiang Cui,et al.  Detecting Malicious URLs via a Keyword-Based Convolutional Gated-Recurrent-Unit Neural Network , 2019, IEEE Access.

[66]  Witold Pedrycz,et al.  Hidden Markov Models Based Approaches to Long-Term Prediction for Granular Time Series , 2018, IEEE Transactions on Fuzzy Systems.

[67]  Min Zuo,et al.  CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture , 2019, Sensors.

[68]  Zhang Hong,et al.  The Application of the Pso Based BP Network in Short-Term Load Forecasting , 2012 .

[69]  Jian Pan,et al.  Recursive Algorithms for Multivariable Output-Error-Like ARMA Systems , 2019, Mathematics.

[70]  Juan Li,et al.  Wind Power Forecasting Based on the BP Neural Network , 2013 .

[71]  Mianxiong Dong,et al.  Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing , 2018, IEEE Network.

[72]  Ting Cui,et al.  Joint Multi-innovation Recursive Extended Least Squares Parameter and State Estimation for a Class of State-space Systems , 2020 .

[73]  Jiabin Yu,et al.  Reliable flight performance assessment of multirotor based on interacting multiple model particle filter and health degree , 2019 .

[74]  R. Biswas,et al.  Short Term Forecasting of Agriculture Commodity Price by Using ARIMA: Based on Indian Market , 2019, Communications in Computer and Information Science.

[75]  Dinesh Bhuriya,et al.  Stock market predication using a linear regression , 2017, 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA).

[76]  Xin Wang,et al.  A smart agriculture IoT system based on deep reinforcement learning , 2019, Future Gener. Comput. Syst..

[77]  Andreas Kamilaris,et al.  Deep learning in agriculture: A survey , 2018, Comput. Electron. Agric..

[78]  Chongguang Li,et al.  Seasonal forecasting of agricultural commodity price using a hybrid STL and ELM method: Evidence from the vegetable market in China , 2018, Neurocomputing.

[79]  Yusuf Yaslan,et al.  Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting , 2017 .

[80]  Feng Ding,et al.  Decomposition- and Gradient-Based Iterative Identification Algorithms for Multivariable Systems Using the Multi-innovation Theory , 2019, Circuits Syst. Signal Process..

[81]  Tingli Su,et al.  Probability Fusion Decision Framework of Multiple Deep Neural Networks for Fine-Grained Visual Classification , 2019, IEEE Access.

[82]  Baihai Zhang,et al.  A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model , 2020, Sensors.