Log In Sign Up

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

by   Bashar Alhnaity, et al.

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.


Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning

Accurate prediction of crop yield supported by scientific and domain-rel...

Machine learning algorithms to infer trait matching and predict species interactions in ecological networks

Ecologists have long suspected that species are more likely to interact ...

Deep learning for laboratory earthquake prediction and autoregressive forecasting of fault zone stress

Earthquake forecasting and prediction have long and in some cases sordid...

DeepAg: Deep Learning Approach for Measuring the Effects of Outlier Events on Agricultural Production and Policy

Quantitative metrics that measure the global economy's equilibrium have ...