# Applying Time Series Deep Learning Models to Forecast the Growth of Perennial Ryegrass in Ireland

arXiv: https://arxiv.org/abs/2511.03749 · HTML: https://mlsanity.com/paper/2511.03749/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 45 references that are in the map.
- First posted on arXiv 4 Nov 2025.

## Builds on (most cited first, 9 of 9)

- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [A guide to state-space modeling of ecological time series](https://mlsanity.com/paper/2002.02001/) (2020)
- [Data Warehouse and Decision Support on Integrated Crop Big Data](https://mlsanity.com/paper/2003.04470/) (2020)
- [Designing and Implementing Data Warehouse for Agricultural Big Data](https://mlsanity.com/paper/1905.12411/) (2019)
- [Semantic Search by Latent Ontological Features](https://mlsanity.com/paper/1807.05576/) (2018)
- [A Graph Based Raman Spectral Processing Technique for Exosome Classification](https://mlsanity.com/paper/2504.15324/) (2025)
- [Enhancing Bagging Ensemble Regression with Data Integration for Time Series-Based Diabetes Prediction](https://mlsanity.com/paper/2506.13786/) (2025)

## Abstract (arXiv)

Grasslands, constituting the world's second-largest terrestrial carbon sink, play a crucial role in biodiversity and the regulation of the carbon cycle. Currently, the Irish dairy sector, a significant economic contributor, grapples with challenges related to profitability and sustainability. Presently, grass growth forecasting relies on impractical mechanistic models. In response, we propose deep learning models tailored for univariate datasets, presenting cost-effective alternatives. Notably, a temporal convolutional network designed for forecasting Perennial Ryegrass growth in Cork exhibits high performance, leveraging historical grass height data with RMSE of 2.74 and MAE of 3.46. Validation across a comprehensive dataset spanning 1,757 weeks over 34 years provides insights into optimal model configurations. This study enhances our understanding of model behavior, thereby improving reliability in grass growth forecasting and contributing to the advancement of sustainable dairy farming practices.