Applying Time Series Deep Learning Models to Forecast the Growth of Perennial Ryegrass in Ireland
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915601525506048 |
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| author | Onibonoje, Oluwadurotimi Ngo, Vuong M. McCarre, Andrew Ruelle, Elodie O-Briend, Bernadette Roantree, Mark |
| author_facet | Onibonoje, Oluwadurotimi Ngo, Vuong M. McCarre, Andrew Ruelle, Elodie O-Briend, Bernadette Roantree, Mark |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03749 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Applying Time Series Deep Learning Models to Forecast the Growth of Perennial Ryegrass in Ireland Onibonoje, Oluwadurotimi Ngo, Vuong M. McCarre, Andrew Ruelle, Elodie O-Briend, Bernadette Roantree, Mark Machine Learning Artificial Intelligence Applications 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. |
| title | Applying Time Series Deep Learning Models to Forecast the Growth of Perennial Ryegrass in Ireland |
| topic | Machine Learning Artificial Intelligence Applications |
| url | https://arxiv.org/abs/2511.03749 |