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Spatial-Temporal Multi-Task Learning for Within-Field Cotton Yield Prediction

  • Long H. NguyenEmail author
  • Jiazhen Zhu
  • Zhe Lin
  • Hanxiang Du
  • Zhou Yang
  • Wenxuan Guo
  • Fang Jin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11439)

Abstract

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.

Notes

Acknowledgement

This work was supported by the U.S. National Science Foundation under the Grant CNS-1737634.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Long H. Nguyen
    • 1
    Email author
  • Jiazhen Zhu
    • 2
  • Zhe Lin
    • 3
  • Hanxiang Du
    • 1
  • Zhou Yang
    • 1
  • Wenxuan Guo
    • 3
  • Fang Jin
    • 1
  1. 1.Department of Computer ScienceTexas Tech UniversityLubbockUSA
  2. 2.Department of Computer ScienceGeorge Washington UniversityWashington, D.C.USA
  3. 3.Department of Plant and Soil ScienceTexas Tech UniversityLubbockUSA

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