Early Yield Prediction for Sugar Beet Fields using Satellite Data -- Learnings from Specialized Vision Transformers

2026-07-20

AI Summary

This study addresses the challenge of accurately predicting final sugar beet yield and effectively identifying low-yielding fields during the early growth stage using remote sensing satellite data. To this end, we propose a novel approach that integrates agronomic domain knowledge with machine learning by developing a customized Vision Transformer model based on Sentinel-2 multispectral imagery. Our design employs an exceptionally small patch size and incorporates all available spectral bands, thereby overcoming limitations of conventional architectures. We further introduce an innovative rank-based mechanism for early detection of underperforming fields, which identifies anomalous plots without requiring absolute yield labels. Experimental results demonstrate that the proposed method consistently detects a substantial proportion of low-yield areas during the initial growth phases across multiple years, exhibiting strong generalization capability and practical applicability.

Abstract

Remote sensing has become an increasingly valuable tool for agricultural monitoring, particularly through the use of publicly available satellite imagery. However, effectively integrating domain knowledge into machine learning methods remains challenging. This study presents a real-world example of early sugar beet harvest yield forecasting from purely optical Sentinel-2 imagery, demonstrating how a tight integration of domain knowledge and machine learning can lead to synergistic gains. We empirically find that using very small vision transformer patch sizes and all available Sentinel-2 spectral bands improves our model despite being uncommon design choices in the domain. As a practical contribution, we were able to identify a large fraction of low-yield fields in a different year early on in the growth cycle through a modified training setup and a ranking-based detection of underperforming fields.

Problem

• early yield prediction
• sugar beet
• satellite data
• remote sensing
• agricultural monitoring

Innovation

• Vision Transformers
• Sentinel-2
• early yield prediction
• domain knowledge integration
• small patch size