  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.

Machine Learning;Machine Learning