用深度学习预测爱尔兰多年生黑麦草生长，精度高且成本低。
Applying Time Series Deep Learning Models to Forecast the Growth of Perennial Ryegrass in Ireland
- 采用时序卷积网络，仅凭历史草高数据进行预测。
- 在1757周数据上表现优异，RMSE为2.74，MAE为3.46。
- 为可持续奶业提供可信赖的草场生长预报工具。
草地是全球第二大陆地碳汇，在生物多样性和碳循环调节中起关键作用。目前，作为重要经济支柱的爱尔兰乳业面临盈利与可持续性挑战。现有草类生长预测依赖不切实际的机理模型。为此，我们提出针对单变量数据集的深度学习模型，提供低成本替代方案。特别地，专为科克地区多年生黑麦草生长设计的时序卷积网络表现突出，仅使用历史草高数据，即实现RMSE 2.74、MAE 3.46。在覆盖34年共1,757周的综合数据集上验证，揭示了最优模型配置。本研究深化了对模型行为的理解，提升了草类生长预测的可靠性，助力可持续奶业发展。
原文摘要 · Abstract (English)
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.
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