CYPRESS: Crop Yield Prediction via Regression on Prithvi's Encoder for Satellite Sensing

Accurate and timely crop yield prediction is crucial for global food security and modern agricultural management. Traditional methods often lack the scalability and granularity required for precision farming. This paper introduces CYPRESS (Crop Yield Prediction via Regression on Prithvi's Encoder for Satellite Sensing), a deep learning model designed for high-resolution, intra-field canola yield prediction. CYPRESS leverages a pre-trained, large-scale geospatial foundation model (Prithvi-EO-2.0-600M) and adapts it for a continuous regression task, transforming multi-temporal satellite imagery into dense, pixel-level yield maps. Evaluated on a comprehensive dataset from the Canadian Prairies, CYPRESS demonstrates superior performance over existing deep learning-based yield prediction models, highlighting the effectiveness of fine-tuning foundation models for specialized agricultural applications. By providing a continuous, high-resolution output, CYPRESS offers a more actionable tool for precision agriculture than conventional classification or county-level aggregation methods. This work validates a novel approach that bridges the gap between large-scale Earth observation and on-farm decision-making, offering a scalable solution for detailed agricultural monitoring.