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

doi:10.1007/978-3-030-16148-4_27

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 proceeding › Conference contribution › peer-review

21 Scopus citations

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.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings
Editors	Min-Ling Zhang, Sheng-Jun Huang, Zhi-Hua Zhou, Qiang Yang, Zhiguo Gong
Publisher	Springer-Verlag
Pages	343-354
Number of pages	12
ISBN (Print)	9783030161477
DOIs	https://doi.org/10.1007/978-3-030-16148-4_27
State	Published - 2019
Event	23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019 - Macau, China Duration: Apr 14 2019 → Apr 17 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11439 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019
Country/Territory	China
City	Macau
Period	04/14/19 → 04/17/19

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10.1007/978-3-030-16148-4_27

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Nguyen, L. H., Zhu, J., Lin, Z., Du, H., Yang, Z., Guo, W., & Jin, F. (2019). Spatial-temporal multi-task learning for within-field cotton yield prediction. In M.-L. Zhang, S.-J. Huang, Z.-H. Zhou, Q. Yang, & Z. Gong (Eds.), Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings (pp. 343-354). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11439 LNAI). Springer-Verlag. https://doi.org/10.1007/978-3-030-16148-4_27

Nguyen, Long H. ; Zhu, Jiazhen ; Lin, Zhe et al. / Spatial-temporal multi-task learning for within-field cotton yield prediction. Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings. editor / Min-Ling Zhang ; Sheng-Jun Huang ; Zhi-Hua Zhou ; Qiang Yang ; Zhiguo Gong. Springer-Verlag, 2019. pp. 343-354 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Spatial-temporal multi-task learning for within-field cotton yield prediction",

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.",

author = "Nguyen, {Long H.} and Jiazhen Zhu and Zhe Lin and Hanxiang Du and Zhou Yang and Wenxuan Guo and Fang Jin",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019 ; Conference date: 14-04-2019 Through 17-04-2019",

year = "2019",

doi = "10.1007/978-3-030-16148-4_27",

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Nguyen, LH, Zhu, J, Lin, Z, Du, H, Yang, Z, Guo, W & Jin, F 2019, Spatial-temporal multi-task learning for within-field cotton yield prediction. in M-L Zhang, S-J Huang, Z-H Zhou, Q Yang & Z Gong (eds), Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11439 LNAI, Springer-Verlag, pp. 343-354, 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019, Macau, China, 04/14/19. https://doi.org/10.1007/978-3-030-16148-4_27

Spatial-temporal multi-task learning for within-field cotton yield prediction. / Nguyen, Long H.; Zhu, Jiazhen; Lin, Zhe et al.
Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings. ed. / Min-Ling Zhang; Sheng-Jun Huang; Zhi-Hua Zhou; Qiang Yang; Zhiguo Gong. Springer-Verlag, 2019. p. 343-354 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11439 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Spatial-temporal multi-task learning for within-field cotton yield prediction

AU - Nguyen, Long H.

AU - Zhu, Jiazhen

AU - Lin, Zhe

AU - Du, Hanxiang

AU - Yang, Zhou

AU - Guo, Wenxuan

AU - Jin, Fang

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

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SN - 9783030161477

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings

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A2 - Huang, Sheng-Jun

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A2 - Gong, Zhiguo

PB - Springer-Verlag

T2 - 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019

Y2 - 14 April 2019 through 17 April 2019

ER -

Nguyen LH, Zhu J, Lin Z, Du H, Yang Z, Guo W et al. Spatial-temporal multi-task learning for within-field cotton yield prediction. In Zhang ML, Huang SJ, Zhou ZH, Yang Q, Gong Z, editors, Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings. Springer-Verlag. 2019. p. 343-354. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-16148-4_27

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

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