Matches in SemOpenAlex for { <https://semopenalex.org/work/W4288289920> ?p ?o ?g. }
Showing items 1 to 87 of
87
with 100 items per page.
- W4288289920 abstract "Effective plant growth and yield prediction is an essential task for greenhouse growers and for agriculture in general. Developing models which can effectively model growth and yield can help growers improve the environmental control for better production, match supply and market demand and lower costs. Recent developments in Machine Learning (ML) and, in particular, Deep Learning (DL) can provide powerful new analytical tools. The proposed study utilises ML and DL techniques to predict yield and plant growth variation across two different scenarios, tomato yield forecasting and Ficus benjamina stem growth, in controlled greenhouse environments. We deploy a new deep recurrent neural network (RNN), using the Long Short-Term Memory (LSTM) neuron model, in the prediction formulations. Both the former yield, growth and stem diameter values, as well as the microclimate conditions, are used by the RNN architecture to model the targeted growth parameters. A comparative study is presented, using ML methods, such as support vector regression and random forest regression, utilising the mean square error criterion, in order to evaluate the performance achieved by the different methods. Very promising results, based on data that have been obtained from two greenhouses, in Belgium and the UK, in the framework of the EU Interreg SMARTGREEN project (2017-2021), are presented." @default.
- W4288289920 created "2022-07-28" @default.
- W4288289920 creator A5001457450 @default.
- W4288289920 creator A5018447003 @default.
- W4288289920 creator A5025545143 @default.
- W4288289920 creator A5036181583 @default.
- W4288289920 date "2019-07-01" @default.
- W4288289920 modified "2023-10-01" @default.
- W4288289920 title "Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments" @default.
- W4288289920 doi "https://doi.org/10.48550/arxiv.1907.00624" @default.
- W4288289920 hasPublicationYear "2019" @default.
- W4288289920 type Work @default.
- W4288289920 citedByCount "0" @default.
- W4288289920 crossrefType "posted-content" @default.
- W4288289920 hasAuthorship W4288289920A5001457450 @default.
- W4288289920 hasAuthorship W4288289920A5018447003 @default.
- W4288289920 hasAuthorship W4288289920A5025545143 @default.
- W4288289920 hasAuthorship W4288289920A5036181583 @default.
- W4288289920 hasBestOaLocation W42882899201 @default.
- W4288289920 hasConcept C105795698 @default.
- W4288289920 hasConcept C108583219 @default.
- W4288289920 hasConcept C118518473 @default.
- W4288289920 hasConcept C119857082 @default.
- W4288289920 hasConcept C12267149 @default.
- W4288289920 hasConcept C127413603 @default.
- W4288289920 hasConcept C134121241 @default.
- W4288289920 hasConcept C147168706 @default.
- W4288289920 hasConcept C152877465 @default.
- W4288289920 hasConcept C154945302 @default.
- W4288289920 hasConcept C169258074 @default.
- W4288289920 hasConcept C18903297 @default.
- W4288289920 hasConcept C191897082 @default.
- W4288289920 hasConcept C192562407 @default.
- W4288289920 hasConcept C201995342 @default.
- W4288289920 hasConcept C2780451532 @default.
- W4288289920 hasConcept C32198211 @default.
- W4288289920 hasConcept C32957820 @default.
- W4288289920 hasConcept C33923547 @default.
- W4288289920 hasConcept C41008148 @default.
- W4288289920 hasConcept C50644808 @default.
- W4288289920 hasConcept C6557445 @default.
- W4288289920 hasConcept C83546350 @default.
- W4288289920 hasConcept C86803240 @default.
- W4288289920 hasConcept C88463610 @default.
- W4288289920 hasConceptScore W4288289920C105795698 @default.
- W4288289920 hasConceptScore W4288289920C108583219 @default.
- W4288289920 hasConceptScore W4288289920C118518473 @default.
- W4288289920 hasConceptScore W4288289920C119857082 @default.
- W4288289920 hasConceptScore W4288289920C12267149 @default.
- W4288289920 hasConceptScore W4288289920C127413603 @default.
- W4288289920 hasConceptScore W4288289920C134121241 @default.
- W4288289920 hasConceptScore W4288289920C147168706 @default.
- W4288289920 hasConceptScore W4288289920C152877465 @default.
- W4288289920 hasConceptScore W4288289920C154945302 @default.
- W4288289920 hasConceptScore W4288289920C169258074 @default.
- W4288289920 hasConceptScore W4288289920C18903297 @default.
- W4288289920 hasConceptScore W4288289920C191897082 @default.
- W4288289920 hasConceptScore W4288289920C192562407 @default.
- W4288289920 hasConceptScore W4288289920C201995342 @default.
- W4288289920 hasConceptScore W4288289920C2780451532 @default.
- W4288289920 hasConceptScore W4288289920C32198211 @default.
- W4288289920 hasConceptScore W4288289920C32957820 @default.
- W4288289920 hasConceptScore W4288289920C33923547 @default.
- W4288289920 hasConceptScore W4288289920C41008148 @default.
- W4288289920 hasConceptScore W4288289920C50644808 @default.
- W4288289920 hasConceptScore W4288289920C6557445 @default.
- W4288289920 hasConceptScore W4288289920C83546350 @default.
- W4288289920 hasConceptScore W4288289920C86803240 @default.
- W4288289920 hasConceptScore W4288289920C88463610 @default.
- W4288289920 hasLocation W42882899201 @default.
- W4288289920 hasLocation W42882899202 @default.
- W4288289920 hasLocation W42882899203 @default.
- W4288289920 hasOpenAccess W4288289920 @default.
- W4288289920 hasPrimaryLocation W42882899201 @default.
- W4288289920 hasRelatedWork W2936214295 @default.
- W4288289920 hasRelatedWork W3001481174 @default.
- W4288289920 hasRelatedWork W3086642004 @default.
- W4288289920 hasRelatedWork W4213142596 @default.
- W4288289920 hasRelatedWork W4220933319 @default.
- W4288289920 hasRelatedWork W4226246648 @default.
- W4288289920 hasRelatedWork W4281386417 @default.
- W4288289920 hasRelatedWork W4281645081 @default.
- W4288289920 hasRelatedWork W4286768673 @default.
- W4288289920 hasRelatedWork W4287669202 @default.
- W4288289920 isParatext "false" @default.
- W4288289920 isRetracted "false" @default.
- W4288289920 workType "article" @default.