  Labeled datasets for agriculture are extremely spatially imbalanced. When
developing algorithms for data-sparse regions, a natural approach is to use
transfer learning from data-rich regions. While standard transfer learning
approaches typically leverage only direct inputs and outputs, geospatial
imagery and agricultural data are rich in metadata that can inform transfer
learning algorithms, such as the spatial coordinates of data-points or the
class of task being learned. We build on previous work exploring the use of
meta-learning for agricultural contexts in data-sparse regions and introduce
task-informed meta-learning (TIML), an augmentation to model-agnostic
meta-learning which takes advantage of task-specific metadata. We apply TIML to
crop type classification and yield estimation, and find that TIML significantly
improves performance compared to a range of benchmarks in both contexts, across
a diversity of model architectures. While we focus on tasks from agriculture,
TIML could offer benefits to any meta-learning setup with task-specific
metadata, such as classification of geo-tagged images and species distribution
modelling.
