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

arXiv: https://arxiv.org/abs/1907.00624 · HTML: https://mlsanity.com/paper/1907.00624/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2019.

## Built on by (most cited first, 9 of 9)

- [The Role of Cross-Silo Federated Learning in Facilitating Data Sharing in the Agri-Food Sector](https://mlsanity.com/paper/2104.07468/) (2021)
- [Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2105.07789/) (2021)
- [Fully Homomorphically Encrypted Deep Learning as a Service](https://mlsanity.com/paper/2107.12997/) (2021)
- [AI-enabled Efficient and Safe Food Supply Chain](https://mlsanity.com/paper/2105.00333/) (2021)
- [A Learned Simulation Environment to Model Plant Growth in Indoor Farming](https://mlsanity.com/paper/2212.03155/) (2022)
- [Corn Yield Prediction Model with Deep Neural Networks for Smallholder Farmer Decision Support System](https://mlsanity.com/paper/2401.03768/) (2024)
- [Predictive Modeling, Pattern Recognition, and Spatiotemporal Representations of Plant Growth in Simulated and Controlled Environments: A Comprehensive Review](https://mlsanity.com/paper/2412.10538/) (2024)
- [Agent-Based Post-Hoc Correction of Agricultural Yield Forecasts](https://mlsanity.com/paper/2605.12375/) (2026)
- [Meta-Learning for Data-Efficient Plant Growth Estimation via Vision Transformers and Fuzzy Clustering](https://mlsanity.com/paper/2609.10749/) (2026)

## Abstract (arXiv)

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.