Modeling and optimization of environment in agricultural greenhouses for improving cleaner and sustainable crop production

[1]  D.H.C Chow,et al.  Methodologies of control strategies for improving energy efficiency in agricultural greenhouses , 2020 .

[2]  Lijun Zhang,et al.  Hierarchical model predictive control of Venlo-type greenhouse climate for improving energy efficiency and reducing operating cost , 2020 .

[3]  Mehran Salehi Shahrabi,et al.  Long-term planning of supplying energy for greenhouses using renewable resources under uncertainty , 2020 .

[4]  Binrui Wang,et al.  Construction of greenhouse environment temperature adaptive model based on parameter identification , 2020, Comput. Electron. Agric..

[5]  I. Laktionov,et al.  Mathematical Model of Measuring Monitoring and Temperature Control of Growing Vegetables in Greenhouses , 2020 .

[6]  Hyoung Seok Kim,et al.  Time-serial analysis of deep neural network models for prediction of climatic conditions inside a greenhouse , 2020, Comput. Electron. Agric..

[7]  Manuel Toledano-Ayala,et al.  Applications of Artificial Neural Networks in Greenhouse Technology and Overview for Smart Agriculture Development , 2020, Applied Sciences.

[8]  Azim Doğuş Tuncer,et al.  Performance enhancement of a greenhouse dryer: Analysis of a cost-effective alternative solar air heater , 2020 .

[9]  Xu Chen,et al.  A fast modeling and optimization scheme for greenhouse environmental system using proper orthogonal decomposition and multi-objective genetic algorithm , 2020, Comput. Electron. Agric..

[10]  Lihong Xu,et al.  An Integrated Yield Prediction Model for Greenhouse Tomato , 2019 .

[11]  Hossam S. Hassanein,et al.  Wireless Sensor Network and Deep Learning For Prediction Greenhouse Environments , 2019, 2019 International Conference on Smart Applications, Communications and Networking (SmartNets).

[12]  Hernández-Salazar Jorge A.,et al.  Estimation of the Evapotranspiration using ANFIS algorithm for Agricultural Production in Greenhouse , 2019, 2019 IEEE International Conference on Applied Science and Advanced Technology (iCASAT).

[13]  James Alistair Fox,et al.  Greenhouse energy management: The thermal interaction of greenhouses with the ground , 2019, Journal of Cleaner Production.

[14]  Tarik Kousksou,et al.  Review on greenhouse microclimate and application: Design parameters, thermal modeling and simulation, climate controlling technologies , 2019, Solar Energy.

[15]  Qian Zhang,et al.  Attention-based recurrent neural networks for accurate short-term and long-term dissolved oxygen prediction , 2019, Comput. Electron. Agric..

[16]  Hua Yang,et al.  Deterministic and stochastic modelling of greenhouse microclimate , 2019, Systems Science & Control Engineering.

[17]  Seyed Majid Sajadiye,et al.  The effect of dynamic solar heat load on the greenhouse microclimate using CFD simulation , 2019, Renewable Energy.

[18]  Liu Yong,et al.  Application of Particle Swarm Optimization BP Algorithm in Air Humidity of Greenhouse Crops , 2019, IOP Conference Series: Materials Science and Engineering.

[19]  Zhenfeng Xu,et al.  Review on Control Methods and Strategies of Greenhouse Microclimate , 2019, DEStech Transactions on Computer Science and Engineering.

[20]  Ling Yang,et al.  DSTP-RNN: a dual-stage two-phase attention-based recurrent neural networks for long-term and multivariate time series prediction , 2019, Expert Syst. Appl..

[21]  A. Guizani,et al.  Autonomous greenhouse microclimate through hydroponic design and refurbished thermal energy by phase change material , 2019, Journal of Cleaner Production.

[22]  S. Ahamed,et al.  Energy saving techniques for reducing the heating cost of conventional greenhouses , 2019, Biosystems Engineering.

[23]  Jean-François Balmat,et al.  Evaluation of the reference evapotranspiration for a greenhouse crop using an Adaptive-Network-Based Fuzzy Inference System (ANFIS) , 2019, ICMLSC.

[24]  Latifa Belhaj Salah,et al.  Deep Elman Neural Network for Greenhouse Modeling , 2018, Smart Innovation, Systems and Technologies.

[25]  Kangil Kim,et al.  Stable Forecasting of Environmental Time Series via Long Short Term Memory Recurrent Neural Network , 2018, IEEE Access.

[26]  Mahesh Chand Singh,et al.  Development of a microclimate model for prediction of temperatures inside a naturally ventilated greenhouse under cucumber crop in soilless media , 2018, Comput. Electron. Agric..

[27]  Kalyanmoy Deb,et al.  Evolving and Comparing Greenhouse Control Strategies using Model-Based Multi-Objective Optimization , 2018, 2018 IEEE Symposium Series on Computational Intelligence (SSCI).

[28]  M. Kacira,et al.  Development and analysis of dynamical mathematical models of greenhouse climate: A review , 2018, European Journal of Horticultural Science.

[29]  Youjun Yue,et al.  The Prediction of Greenhouse Temperature and Humidity Based on LM-RBF Network , 2018, 2018 IEEE International Conference on Mechatronics and Automation (ICMA).

[30]  T. K. Radhakrishnan,et al.  Modeling of greenhouse agro-ecosystem using optimally designed bootstrapping artificial neural network , 2018, Neural Computing and Applications.

[31]  Jung Eek Son,et al.  Forecasting Root-Zone Electrical Conductivity of Nutrient Solutions in Closed-Loop Soilless Cultures via a Recurrent Neural Network Using Environmental and Cultivation Information , 2018, Front. Plant Sci..

[32]  B. Mohammadi,et al.  Application of dynamic model to predict some inside environment variables in a semi-solar greenhouse , 2018, Information Processing in Agriculture.

[33]  Jing Hua,et al.  A knowledge-and-data-driven modeling approach for simulating plant growth and the dynamics of CO2/O2 concentrations in a closed system of plants and humans by integrating mechanistic and empirical models , 2018, Comput. Electron. Agric..

[34]  G. Aiello,et al.  A decision support system based on multisensor data fusion for sustainable greenhouse management , 2018 .

[35]  Wei Zhou,et al.  Multi-objective optimization of fan-pad system operation for venlo greenhouse using CFD model based data interactive mechanism , 2017, 2017 36th Chinese Control Conference (CCC).

[36]  Zheng Hui,et al.  Modeling and simulation of greenhouse temperature hybrid system based on ARMAX model , 2017, 2017 36th Chinese Control Conference (CCC).

[37]  Tag Gon Kim,et al.  Data modeling versus simulation modeling in the big data era: case study of a greenhouse control system , 2017, Simul..

[38]  Minzan Li,et al.  Proactive energy management of solar greenhouses with risk assessment to enhance smart specialisation in China , 2017 .

[39]  Garrison W. Cottrell,et al.  A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction , 2017, IJCAI.

[40]  Lihong Xu,et al.  Towards discrete time model for greenhouse climate control , 2017 .

[41]  L. Ting,et al.  Universality of an improved photosynthesis prediction model based on PSO-SVM at all growth stages of tomato , 2017 .

[42]  Zhifang Liu,et al.  Proximal Support Vector Machine Improvement and Its Application to the Environmental Monitoring of Greenhouse Plant Growth , 2017 .

[43]  S. Saeedeh Sadegh,et al.  Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm , 2016 .

[44]  Shihua Li,et al.  Design and Research of Intelligent Greenhouse Monitoring System Based on Internet of Things , 2016 .

[45]  Shahbaz Gul Hassan,et al.  Prediction of the temperature in a Chinese solar greenhouse based on LSSVM optimized by improved PSO , 2016, Comput. Electron. Agric..

[46]  Shaojin Wang,et al.  CFD and weighted entropy based simulation and optimisation of Chinese Solar Greenhouse temperature distribution , 2016 .

[47]  Jiangxin Yang,et al.  Energy demand forecasting of the greenhouses using nonlinear models based on model optimized prediction method , 2016, Neurocomputing.

[48]  E. Heuvelink,et al.  A knowledge-and-data-driven modeling approach for simulating plant growth: A case study on tomato growth , 2015 .

[49]  Radiša Jovanović,et al.  Ensemble of various neural networks for prediction of heating energy consumption , 2015 .

[50]  Ammar A. Farhan,et al.  A dynamic model and an experimental study for the internal air and soil temperatures in an innovative greenhouse , 2015 .

[51]  Javier Ruiz-León,et al.  Modeling of a greenhouse prototype using PSO algorithm based on a LabViewTM application , 2014, 2014 11th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE).

[52]  Javier Ruiz-León,et al.  Modeling of a greenhouse using Particle Swarm Optimization , 2013, 2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE).

[53]  Tadeusz P. Dobrowiecki,et al.  Hybrid MLP-RBF model structure for short-term internal temperature prediction in greenhouse environments , 2013, 2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI).

[54]  Zhongyi Hu,et al.  A PSO and pattern search based memetic algorithm for SVMs parameters optimization , 2013, Neurocomputing.

[55]  K. Sumathy,et al.  Thermal modeling aspects of solar greenhouse microclimate control: A review on heating technologies , 2013 .

[56]  Antonio Ramírez-Treviño,et al.  Greenhouse Modeling Using Continuous Timed Petri Nets , 2013 .

[57]  Irineo L. López-Cruz,et al.  Modelling greenhouse air temperature using evolutionary algorithms in auto regressive models , 2013 .

[58]  E. J. van Henten,et al.  A methodology for model-based greenhouse design: Part 2, description and validation of a tomato yield model , 2011 .

[59]  C. Stanghellini,et al.  A methodology for model-based greenhouse design: Part 1, a greenhouse climate model for a broad range of designs and climates , 2011 .

[60]  P. Eredics,et al.  Hybrid knowledge modeling for an intelligent greenhouse , 2010, IEEE 8th International Symposium on Intelligent Systems and Informatics.

[61]  Mohammad Reza Yousefi,et al.  A hybrid neuro-fuzzy approach for greenhouse climate modeling , 2010, 2010 5th IEEE International Conference Intelligent Systems.

[62]  Keqi Wang,et al.  Study on Greenhouse Environment Neural Network Model Based on PSO Algorithm , 2010, 2010 International Conference on Intelligent Computing and Cognitive Informatics.

[63]  Chengwei Ma,et al.  Modeling greenhouse air humidity by means of artificial neural network and principal component analysis , 2010 .

[64]  J. J. García-Escalante,et al.  Calibration of a greenhouse climate model using evolutionary algorithms , 2009 .

[65]  Maohua Wang,et al.  Support vector machines regression and modeling of greenhouse environment , 2009 .

[66]  Cheng-Lung Huang,et al.  A distributed PSO-SVM hybrid system with feature selection and parameter optimization , 2008, Appl. Soft Comput..

[67]  António E. Ruano,et al.  Application of computational intelligence methods to greenhouse environmental modelling , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[68]  Xavier Blasco,et al.  Robust identification of non-linear greenhouse model using evolutionary algorithms , 2008 .

[69]  V. M. Salokhe,et al.  Modelling of tropical greenhouse temperature by auto regressive and neural network models , 2008 .

[70]  Xavier Blasco,et al.  Non-linear robust identification of a greenhouse model using multi-objective evolutionary algorithms , 2007 .

[71]  Ying Chen,et al.  Greenhouse Air Temperature and Humidity Prediction Based on Improved BP Neural Network and Genetic Algorithm , 2007, ISNN.

[72]  Frédéric Lafont,et al.  Fuzzy identification of a greenhouse , 2007, Appl. Soft Comput..

[73]  Cheng-Lung Huang,et al.  A GA-based feature selection and parameters optimizationfor support vector machines , 2006, Expert Syst. Appl..

[74]  J. Boaventura Cunha,et al.  Greenhouse air temperature predictive control using the particle swarm optimisation algorithm , 2005 .

[75]  Paulo Salgado,et al.  Greenhouse climate hierarchical fuzzy modelling , 2005 .

[76]  Raphael Linker,et al.  Greenhouse temperature modeling: a comparison between sigmoid neural networks and hybrid models , 2004, Math. Comput. Simul..

[77]  Jan Pieters,et al.  Modelling Greenhouse Temperature by means of Auto Regressive Models , 2003 .

[78]  Sayan Mukherjee,et al.  Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.

[79]  Mark T. Brown,et al.  Improving agricultural sustainability: the case of Swedish greenhouse tomatoes , 1999 .

[80]  Per-Olof Gutman,et al.  Robust Failure Detection and Identification in a Greenhouse Modeled with Hybrid Physical/Neural Network Models , 1998 .

[81]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[82]  Sheng Chen,et al.  Regularized orthogonal least squares algorithm for constructing radial basis function networks , 1996 .

[83]  L. Ljung Convergence analysis of parametric identification methods , 1978 .

[84]  L. A. Zadeh,et al.  From Circuit Theory to System Theory , 1962, Proceedings of the IRE.

[85]  R. Roshandel,et al.  Optimal design for solar greenhouses based on climate conditions , 2020 .

[86]  Fawad Khan,et al.  EVALUATING DIFFERENT MODELS USED FOR PREDICTING THE INDOOR MICROCLIMATIC PARAMETERS OF A GREENHOUSE , 2020 .

[87]  Anthony Denzer,et al.  Energy efficient operation and modeling for greenhouses: A literature review , 2020 .

[88]  José M. Cecilia,et al.  An LSTM Deep Learning Scheme for Prediction of Low Temperatures in Agriculture , 2019, Intelligent Environments.

[89]  A. Selmani,et al.  Particle Swarm Optimization of BP-ANN Based Soft Sensor for Greenhouse Climate , 2018, J. Electron. Commer. Organ..

[90]  Javier Ruiz-León,et al.  Modeling of a greenhouse prototype using PSO and differential evolution algorithms based on a real-time LabView™ application , 2018, Appl. Soft Comput..

[91]  Lihong Xu,et al.  Energy Consumption Prediction of a Greenhouse and Optimization of Daily Average Temperature , 2018 .

[92]  Li Li,et al.  Recurrent Neural Network Model for Prediction of Microclimate in Solar Greenhouse , 2018 .

[93]  S. Grabarczyk Modeling of heat consumption in a greenhouse using experimental data , 2018 .

[94]  Liang Meihui,et al.  Greenhouse temperature predictive control for energy saving using switch actuators , 2018 .

[95]  He Yaofeng,et al.  Greenhouse modelling and control based on T-S model , 2018 .

[96]  P. J. García Nieto,et al.  A hybrid PSO optimized SVM-based model for predicting a successful growth cycle of the Spirulina platensis from raceway experiments data , 2016, J. Comput. Appl. Math..

[97]  Abbas Rohani,et al.  Heat transfer and MLP neural network models to predict inside environment variables and energy lost in a semi-solar greenhouse , 2016 .

[98]  Jian Shen,et al.  A WSN-based prediction model of microclimate in a greenhouse using extreme learning approaches , 2016, 2016 18th International Conference on Advanced Communication Technology (ICACT).

[99]  J. Singh,et al.  Greenhouse microclimate modeling under cropped conditions-A review , 2016 .

[100]  Belkacem Draoui,et al.  Optimization of Greenhouse Climate Model Parameters Using Particle Swarm Optimization and Genetic Algorithms , 2011 .

[101]  J. Ríos-Moreno,et al.  Greenhouse energy consumption prediction using neural networks models , 2009 .

[102]  Xavier Blasco,et al.  Model-based predictive control of greenhouse climate for reducing energy and water consumption , 2007 .

[103]  Jan G. Pieters,et al.  Modelling greenhouse temperature using system identification by means of neural networks , 2004, Neurocomputing.

[104]  José Boaventura Cunha,et al.  GREENHOUSE CLIMATE MODELS: AN OVERVIEW , 2003 .

[105]  Shaojin Wang,et al.  Predicting the Microclimate in a Naturally Ventilated Plastic House in a Mediterranean Climate , 2000 .

[106]  R. Avissar,et al.  Verification Study of a Numerical Greenhouse Microclimate Model , 1982 .

[107]  L. L. Boyd,et al.  Dynamic Simulation of Plant Growth and Environment in the Greenhouse , 1971 .