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

TL;DR

This paper explores the use of deep learning, specifically LSTM-based RNNs, to predict plant growth and yield in greenhouses, demonstrating promising results compared to traditional ML methods across two different scenarios.

Contribution

It introduces a novel LSTM-based RNN model for greenhouse plant growth and yield prediction, outperforming other ML methods in two real-world case studies.

Findings

LSTM RNN achieved lower mean square error than support vector regression.

Deep learning models effectively captured the relationship between microclimate and plant growth.

Results validated with data from greenhouses in Belgium and the UK.

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.