Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments

Bashar Alhnaity, Simon Pearson, Georgios Leontidis, Stefanos Kollias

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

11 Citations (Scopus)
89 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationActa Horticulturae
Subtitle of host publicationGreensys 2019 - International Symposium on Advanced Technologies and Management for Innovative Greenhouses
PublisherInternational Society for Horticultural Science
Number of pages7
ISBN (Electronic) 2406-6168
ISBN (Print)0567-7572
Publication statusPublished - 23 Nov 2020

Publication series

NameActa Horticulturae
ISSN (Print)0567-7572


  • Deep learning
  • Ficus
  • Growth
  • Prediction
  • Recurrent LSTM neural networks
  • Stem diameter
  • Tomato
  • Yield rate


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