用卫星图像提前预测甜菜产量，融合领域知识提升预测精度。
Early Yield Prediction for Sugar Beet Fields using Satellite Data -- Learnings from Specialized Vision Transformers
- 采用小块视觉变换器和全谱段数据，突破常规设计
- 早期识别出大量低产田，准确率显著提升
- 适合农业监测与精准种植研究者参考
遥感技术正日益成为农业监测的重要工具，尤其依赖公开的卫星影像。然而，如何有效将领域知识融入机器学习方法仍具挑战。本研究基于纯光学的Sentinel-2影像，实现了甜菜早起产量预测，展示了领域知识与机器学习紧密结合带来的协同增益。实证发现，使用极小的视觉变换器补丁尺寸并利用所有Sentinel-2光谱波段，尽管在该领域不常见，却显著提升了模型性能。作为实际贡献，通过改进训练策略与基于排名的检测方法，可在生长周期早期识别出不同年份中大量低产田。
原文摘要 · Abstract (English)
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.
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