Remote sensing and artificial intelligence are pivotal technologies of precision agriculture nowadays. The efficient retrieval of large-scale field imagery combined with machine learning techniques shows success in various tasks like phenotyping, weeding, cropping, and disease control. This work will introduce a machine learning framework for automatized large-scale plant-specific trait annotation for the use case disease severity scoring for Cercospora Leaf Spot (CLS) in sugar beet. With concepts of Deep Label Distribution Learning (DLDL), special loss functions, and a tailored model architecture, we develop an efficient Vision Transformer based model for disease severity scoring called SugarViT. One novelty in this work is the combination of remote sensing data with environmental parameters of the experimental sites for disease severity prediction. Although the model is evaluated on this special use case, it is held as generic as possible to also be applicable to various image-based classification and regression tasks. With our framework, it is even possible to learn models on multi-objective problems as we show by a pretraining on environmental metadata.

通过远程遥感和人工智能技术，本文介绍了一种用于大规模病害严重程度评分的自动化机器学习框架，结合了深度标签分布学习、特殊损失函数和定制模型架构的SugarViT模型。同时，本框架还可以适用于各种基于图像的分类和回归任务。

SugarViT -- Multi-objective Regression of UAV Images with Vision
  Transformers and Deep Label Distribution Learning Demonstrated on Disease
  Severity Prediction in Sugar Beet

SugarViT -- 基于视觉 Transformer 和深度标签分布学习的无人机图像多目标回归在甜菜病害严重性预测中的应用