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 - Funding Information:
Acknowledgement. This work was supported by the U.S. National Science Foundation under the Grant CNS-1737634.
Funding Information:
This work was supported by the U.S. National Science Foundation under the Grant CNS-1737634.
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.
UR - http://www.scopus.com/inward/record.url?scp=85064926665&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-16148-4_27
DO - 10.1007/978-3-030-16148-4_27
M3 - Conference contribution
AN - SCOPUS:85064926665
SN - 9783030161477
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 343
EP - 354
BT - Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings
A2 - Zhang, Min-Ling
A2 - Huang, Sheng-Jun
A2 - Zhou, Zhi-Hua
A2 - Yang, Qiang
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 -