Spatial-temporal multi-task learning for within-field cotton yield prediction

Long H. Nguyen, Jiazhen Zhu, Zhe Lin, Hanxiang Du, Zhou Yang, Wenxuan Guo, Fang Jin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

18 Scopus citations


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.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings
EditorsMin-Ling Zhang, Sheng-Jun Huang, Zhi-Hua Zhou, Qiang Yang, Zhiguo Gong
Number of pages12
ISBN (Print)9783030161477
StatePublished - 2019
Event23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019 - Macau, China
Duration: Apr 14 2019Apr 17 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11439 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019


Dive into the research topics of 'Spatial-temporal multi-task learning for within-field cotton yield prediction'. Together they form a unique fingerprint.

Cite this