Spatial-Temporal Multi-Task Learning for Within-Field Cotton Yield Prediction
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.
Long H.Nguyen Jiazhen Zhu Zhe Lin Hanxiang Du Zhou Yang Wenxuan Guo Fang Jin
Department of Computer Science,Texas Tech University,Lubbock,USA Department of Computer Science,George Washington University,Washington,D.C.,USA Department of Plant and Soil Science,Texas Tech University,Lubbock,USA
国际会议
澳门
英文
343-354
2019-04-14（万方平台首次上网日期，不代表论文的发表时间）