发表机构
Center for Artificial Intelligence and Robotics, Technical University of Applied Sciences Wuerzburg-Schweinfurt; Bielefeld University; green spin GmbH（人工智能与机器人中心，维尔茨堡-施韦因富特应用技术大学; 比勒费尔德大学; 绿旋有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究利用卫星数据预测甜菜田早期产量，通过将领域知识与机器学习紧密结合，采用小视觉Transformer补丁大小和所有哨兵2光谱带，改进模型，并能在生长周期早期识别大部分低产田。
AI 中文摘要
遥感已成为农业监测中越来越有价值的工具，特别是通过使用公开可用的卫星图像。然而，将领域知识有效整合到机器学习方法中仍然具有挑战性。本研究展示了一个从纯光学哨兵2图像进行甜菜早期收获产量预测的实际例子，证明了领域知识和机器学习的紧密结合如何带来协同增益。我们通过实验发现，使用非常小的视觉Transformer补丁大小和所有可用的哨兵2光谱带，尽管在该领域是不常见的设计选择，但能改进我们的模型。作为实际贡献，通过改进的训练设置和基于排名的表现不佳领域检测，我们能够在生长周期早期识别出不同年份中大部分低产田。
英文摘要
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.
