2025 / Ongoing research

Optimizing Future Environmental Embedding Representations for Crop Performance

Asif Haque

This work explores how to learn and optimize representations of future environmental conditions, such as climate and soil signals, so that machine learning models can more accurately predict crop performance. The goal is to design embeddings that generalize across regions and seasons, improving forecasting under shifting conditions and supporting data-driven agricultural decisions.

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Overview

Research Direction

The goal is to design embeddings that generalize across regions and seasons, improving forecasting under shifting conditions and supporting data-driven agricultural decisions.