FARM: Crop Yield Prediction via Regression on Prithvi’s Encoder for Satellite Sensing

Abstract

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 FARM (Fine-tuning Agricultural Regression Models), a deep learning framework designed for high-resolution, intra-field canola yield prediction. FARM 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 (30 m) yield maps. Evaluated on a comprehensive dataset from the Canadian Prairies, FARM achieves a Root Mean Squared Error (RMSE) of 0.44 and an R2 of 0.81. Using an independent high-resolution yield monitor dataset, we further show that fine-tuning FARM on limited ground-truth labels outperforms training the same architecture from scratch, confirming the benefit of pre-training on large, upsampled county-level data for data-scarce precision agriculture. These results represent improvement over baseline architectures like 3D-CNN and DeepYield, which highlight the effectiveness of fine-tuning foundation models for specialized agricultural applications. By providing a continuous, high-resolution output, FARM 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.

Plant phenotyping relevance

衛星画像から圃場内の作物収量を推定する回帰手法を開発し、独立データで検証しており、植物の収量形質の取得・推定が中心である。

Code and data availability

The supplied blocks describe the FARM crop yield prediction framework and its datasets (Sentinel-2 HLS imagery, upsampled county-level yield labels, and a private high-resolution yield monitor dataset), but contain no public data deposit, code repository, model checkpoint release, or availability statement. The yield/遥

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