CC BY 4.0Günder, MauriceMauriceGünderIspizua Yamati, Facundo RamónFacundo RamónIspizua YamatiBarreto, AbelAbelBarretoMahlein, Anne-KatrinAnne-KatrinMahleinSifa, RafetRafetSifaBauckhage, ChristianChristianBauckhageZeashan Hameed Khan2025-02-242025-02-242025-02-13https://doi.org/10.24406/publica-4307https://publica.fraunhofer.de/handle/publica/48418610.1371/journal.pone.031809710.24406/publica-4307Remote 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 of 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. Furthermore, we perform several comparison experiments with state-of-the-art methods and models to constitute our modeling and preprocessing choices.enRemote SensingPrecision AgricultureMachine LearningComputer VisionVision TransformerLabel Distribution LearningCercospora Leaf SpotSugar Beetjournal article