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

TL;DR

This paper explores the application of deep learning models, especially temporal convolutional networks, to forecast Perennial Ryegrass growth in Ireland, aiming to improve sustainability in dairy farming by providing accurate, cost-effective predictions.

Contribution

It introduces deep learning models tailored for grass growth forecasting, demonstrating high performance and offering insights into optimal configurations for sustainable agriculture.

Findings

Temporal convolutional network achieved RMSE of 2.74 and MAE of 3.46.

Validation over 1,757 weeks shows reliable model performance.

Enhances grass growth prediction accuracy for sustainable dairy practices.

Abstract

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.