Spatial-temporal Multi-Task Learning for Within-field Cotton Yield Prediction
This work addresses the problem of optimizing crop inputs like irrigation and fertilizer for farmers and agricultural managers, though it appears incremental as it builds on existing multi-task learning approaches.
The paper tackled within-field cotton yield prediction by developing a spatial-temporal multi-task learning algorithm that integrates multiple data sources and uses a weighted regularizer, achieving results that consistently outperform conventional methods.
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 algorithms 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.