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Long short-term memory for a model-free estimation of macronutrient ion concentrations of root-zone in closed-loop soilless cultures

Taewon Moon, Tae In Ahn, Jung Eek Son
2019 Plant Methods  
The trained LSTM can estimate macronutrient ion concentrations in closed-loop soilless cultures using environmental and growth data.  ...  Recently, deep learning has been used to draw meaningful results from nonlinear data and long short-term memory (LSTM) is showing state-of-the-art results in analyzing time-series data.  ...  Missing environmental data were also interpolated using linear interpolation, including missing values of EC and pH which were interpolated using manually-monitored data.  ... 
doi:10.1186/s13007-019-0443-7 pmid:31160918 pmcid:PMC6540585 fatcat:5peomnhr6nd77d5kuzbxokfv2e

Joint Communication and Sensing: A Proof of Concept and Datasets for Greenhouse Monitoring Using LoRaWAN

Ritesh Kumar Singh, Mohammad Hasan Rahmani, Maarten Weyn, Rafael Berkvens
2022 Sensors  
This work uses a multilayer perceptron artificial neural network to predict the weekly plant growth, trained using RSSI value from sensor data and manual measurement of plant height from the greenhouse  ...  We developed this proof of concept of joint communication and sensing by using generated dataset from the "Proefcentrum Hoogstraten" greenhouse in Belgium.  ...  Data Availability Statement: The dataset presented and used in this study are openly available in Zenodo at DOI: 10.5281/zenodo.5793684; Link: https://zenodo.org/record/5793685#.YgLhgPXMK3L.  ... 
doi:10.3390/s22041326 pmid:35214228 pmcid:PMC8963007 fatcat:24n5duveofcphhzxl4s2h2onuy

Retrieval of carbon dioxide vertical concentration profiles from satellite data using artificial neural networks

A. Roberto Carvalho, F. Manoel Ramos, J. Carlos Carvalho
2010 TEMA  
A MultiLayer Perceptron (MLP) ANN was implemented and tested for a large and diversified dataset.  ...  In this paper, we derive vertical distributions of carbon dioxide atmospheric concentration from satellite data using a retrieval algorithm based on an artificial neural network (ANN) technique.  ...  To solve the problem of the CO 2 concentration retrieval we developed an algorithm, written in Java language, that implements a Multilayer perceptron ANN ( [11] and [1] ).  ... 
doi:10.5540/tema.2010.011.03.0205 fatcat:konzila7p5cw3msdvttiokheni

Retrieval of carbon dioxide vertical concentration profiles from satellite data using artificial neural networks

A. Roberto Carvalho, F. Manoel Ramos, J. Carlos Carvalho
2011 TEMA  
A MultiLayer Perceptron (MLP) ANN was implemented and tested for a large and diversified dataset.  ...  In this paper, we derive vertical distributions of carbon dioxide atmospheric concentration from satellite data using a retrieval algorithm based on an artificial neural network (ANN) technique.  ...  To solve the problem of the CO 2 concentration retrieval we developed an algorithm, written in Java language, that implements a Multilayer perceptron ANN ( [11] and [1] ).  ... 
doi:10.5540/tema.2011.011.03.0205 fatcat:ftk4vhai6jhqbioqih72ic5zbu

Understanding the drivers of marine liquid-water cloud occurrence and properties with global observations using neural networks

Hendrik Andersen, Jan Cermak, Julia Fuchs, Reto Knutti, Ulrike Lohmann
2017 Atmospheric Chemistry and Physics  
The statistical models used are shown to be capable of predicting clouds, especially in regions of high cloud variability.  ...  In this study, 15 years (2001–2015) of monthly satellite-retrieved near-global aerosol products are combined with reanalysis data of various meteorological parameters to predict satellite-derived marine  ...  Multilayer perceptrons are a specific type of neural network that are commonly used in the atmospheric sciences and environmental sciences in general, as they are able to model highly nonlinear functions  ... 
doi:10.5194/acp-17-9535-2017 fatcat:nmlchna5qja4lhlxl2mxpba2ie

Understanding the drivers of marine liquid-water cloud occurrence and properties with global observations using neural networks

Hendrik Andersen, Jan Cermak, Julia Fuchs, Reto Knutti, Ulrike Lohmann
2017 Atmospheric Chemistry and Physics Discussions  
The statistical models used are shown to be capable of predicting clouds, especially in regions of high cloud variability.  ...  In this study, 15 years (2001–2015) of monthly satellite-retrieved nearly-global aerosol products are combined with reanalysis data of various meteorological parameters to predict satellite-derived  ...  Multilayer perceptrons are a specific type of neural network that are commonly used in the atmospheric sciences and environmental sciences in general, as they are able to model highly nonlinear functions  ... 
doi:10.5194/acp-2017-282 fatcat:eyxetgmxofgv7ovlpmwnqbm6ji

DATA-BASED PREDICTION OF SOOT EMISSIONS FOR TRANSIENT ENGINE OPERATION

Michèle Schaub
2019 Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska  
This paper focusses on the data-based modelling of soot for transient engine operation in order to predict air pollution in the context of a sophisticated manoeuvring assistance system.  ...  Global vessel traffic is one of the origins responsible for air pollution. Annex VI of the IMO International Convention for the Prevention of Pollution from Ships (MARPOL) focusses on air pollution.  ...  [10] Ship model and engine module For the integration of the data-based model an already existing simulation environment, called SAMMON, is used.  ... 
doi:10.35784/iapgos.29 fatcat:cnguxzpvtbbslgu7wclhkvd6se

Estimación de la fotosíntesis foliar en jitomate bajo invernadero mediante redes neuronales artificiales

José Manuel Vargas Sállago, Irineo Lorenzo López Cruz, Enrique Rico García
2018 Revista Mexicana de Ciencias Agrícolas  
it was necessary to make a linear interpolation of the data to the phyto-monitor to generate two extra points, thus having, data of all the equipment's variables every 10 min.  ...  A network of multi-layer perceptron architecture is used in an ANN. It consists of three layers: incoming calls, hidden and output.  ... 
doi:10.29312/remexca.v3i7.1334 fatcat:gtidewk2una4dgbmru76ozf7o4

Real time estimation of emissions in a diesel vehicle with neural networks

Donateo Teresa, Filomena Riccardo
2020 E3S Web of Conferences  
The second one models the engine block using as input the ambient conditions, the load and the rpm of the engine as derived from the OBD-II scanner.  ...  To solve this problem, the present work proposes a Neural Network model based on the interpolation of the time-histories of driving conditions (speed, altitude, ambient temperature, humidity and pressure  ...  For this reason, we chose a multilayer perceptron feedforward ANN that is characterized by a simple structure.  ... 
doi:10.1051/e3sconf/202019706020 doaj:3244ff324a574afb84e63941e098da1d fatcat:xu675wtcbvg6ho6h4phsko777y

Integrated model for predicting rice yield with climate change

Jin-Ki Park, Amrita Das, Jong-Hwa Park
2018 International Agrophysics  
Due importance was given to scaling up the input parameters using spatial interpolation and GIS and minimising the sources of error in every step of the modelling.  ...  This paper describes a novel approach for predicting rice yield using artificial neural network, spatial interpolation, remote sensing and GIS methods.  ...  The IBM SPSS ANN multilayer perceptron (MLP) model uses a supervised learning technique and has a feedforward architecture.  ... 
doi:10.1515/intag-2017-0010 fatcat:wudxzufjbvcn3i4bmpdlgr6pvm

Applied machine learning in greenhouse simulation; new application and analysis

Morteza Taki, Saman Abdanan Mehdizadeh, Abbas Rohani, Majid Rahnama, Mostafa Rahmati-Joneidabad
2018 Information Processing in Agriculture  
Black box method Energy lost Environmental situation Energy exchange A B S T R A C T Prediction the inside environment variables in greenhouses is very important because they play a vital role in greenhouse  ...  Generalizability and stability of the RBF model with 5-fold cross validation analysis showed that this method can use with small size of data groups.  ...  Acknowledgments The authors would like to thank the editor in chief and the anonymous referees for their valuable suggestions and useful comments that improved the paper content substantially.  ... 
doi:10.1016/j.inpa.2018.01.003 fatcat:hi56lxp3wzck3issk55kxewhai

Statistical Downscaling of Rainfall Under Climate Change in Krishna River Sub-basin of Andhra Pradesh, India Using Artificial Neural Network (ANN)

K.V.R. Satya Sai, S. Krishnaiah, A. Manjunath
2021 Nature Environment and Pollution Technology  
The study uses the Canadian Earth System Model (CanESM2) of the IPCC Fifth Assessment Report, re-analysis from the National Centre for Environmental Prediction (NCEP) as GCM model, and observed rainfall  ...  data as the observed rainfall.  ...  In case of any missing rainfall data in IMD database, the linear interpolation method is used to find the missing data.  ... 
doi:10.46488/nept.2021.v20i02.043 fatcat:muairg7r2ncxbaxwpbrivchdpi

AI-Enabled Efficient and Safe Food Supply Chain

Ilianna Kollia, Jack Stevenson, Stefanos Kollias
2021 Electronics  
In particular, these concern: (i) predicting plant growth and tomato yield in greenhouses, thus matching food production to market needs and reducing food waste or food unavailability; (ii) optimizing  ...  The paper focuses on effective food production, food maintenance energy management and food retail packaging labeling control, using recent advances in machine learning.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/electronics10111223 fatcat:dx67p7xiajf4xflpyrrzcaubdy

AI-enabled Efficient and Safe Food Supply Chain [article]

Ilianna Kollia and Jack Stevenson and Stefanos Kollias
2021 arXiv   pre-print
Recent advances in machine and deep learning are used for effective food production, energy management and food labeling.  ...  In particular, these concern: (i) predicting plant growth and tomato yield in greenhouses, thus matching food production to market needs and reducing food waste or food unavailability; (ii) optimizing  ...  Food Production in Greenhouse Environments Plant Growth Prediction Machine learning techniques have been used to predict growth of Ficus plants using data collected from four cultivation tables in a  ... 
arXiv:2105.00333v1 fatcat:45sy5yjjfjeg7oozqq37s46lmu

CONNECTIONIST MODELLING FOR ANTHROPOGENIC GREENHOUSE GASES (GHG) EMISSIONS IN URBAN ENVIRONMENTS

M. AL-HARBI, M. BIN SHAMS, I. ALHAJRI
2020 Applied Ecology and Environmental Research  
.: Connectionist modelling for anthropogenic greenhouse gases (GHG) emissions in urban environments -2087 -APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 18(2):2087-2107. Abstract.  ...  Global warming induced by greenhouse gases (GHGs) is already a reality and will continue to increase resulting in a severe climate change. The aim of the paper is twofold.  ...  The authors would like to thank Kuwait University and Kuwait Environment Public Authority (KUEPA) for their assitance in data measurments and for continuous support.  ... 
doi:10.15666/aeer/1802_20872107 fatcat:kkxkiyuruva77ouge36gjf4exy
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