  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.
