Spatial-temporal Multi-Task Learning for Within-field Cotton Yield Prediction
Spatial-temporal Multi-Task Learning for Within-field Cotton Yield Prediction
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DOI：
10.1007/978-3-030-16148-4_27
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
L. Nguyen;Jiajie Zhen;Zhe Lin;Hanxiang Du;Zhou Yang;Wenxuan Guo;Fang Jin
中科院分区：
文献类型：
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作者：
L. Nguyen;Jiajie Zhen;Zhe Lin;Hanxiang Du;Zhou Yang;Wenxuan Guo;Fang Jin
了解和准确预测作物产量的田间空间变异性对于作物灌溉水和肥料等投入的定点管理以优化作物生产具有关键作用。然而，作物生长与环境和管理因素（如气候、土壤条件、耕作和灌溉）之间复杂的相互作用对这一任务提出了挑战。在本文中，我们提出了一种新的时空多任务学习算法在西德克萨斯州2001年至2003年的田间作物产量预测。该算法集成多个异构数据源，同时学习不同的功能，并通过引入加权正则化的损失函数聚集时空特征。我们的综合实验结果始终优于其他传统方法的结果，并提出了一种有前途的方法，改善了作物预测研究领域的景观。
Understanding and accurately predicting within-field spatial variability of crop yield play a key role in site-specific management of crop inputs such as irrigation water and fertilizer for optimized crop production. However, such a task is challenged by the complex interaction between crop growth and environmental and managerial factors, such as climate, soil conditions, tillage, and irrigation. In this paper, we present a novel Spatial-temporal Multi-Task Learning algorithm for within-field crop yield prediction in west Texas from 2001 to 2003. This algorithm integrates multiple heterogeneous data sources to learn different features simultaneously, and to aggregate spatial-temporal features by introducing a weighted regularizer to the loss functions. Our comprehensive experimental results consistently outperform the results of other conventional methods, and suggest a promising approach, which improves the landscape of crop prediction research fields.