Spatial-temporal Multi-Task Learning for Within-#12;field Cotton Yield Prediction
Author
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.
Year of Publication
2019
Conference Name
Pacific-Asia Conference on Knowledge Discovery and Data Mining
Date Published
04