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.

翻译：遥感与人工智能是当今精准农业的关键技术。大规模田间影像的高效获取结合机器学习技术，在表型分析、除草、作物收割及病害防控等多项任务中已展现出成功应用。本研究提出一种面向大规模植物特异性性状自动标注的机器学习框架，以甜菜褐斑病（Cercospora Leaf Spot, CLS）严重程度评分作为应用案例。通过融合深度标签分布学习（Deep Label Distribution Learning, DLDL）概念、特殊损失函数及定制化模型架构，我们开发了一种基于视觉变换器的高效病害严重程度评分模型——SugarViT。本研究的创新点之一，在于将遥感数据与试验田环境参数相结合用于病害严重程度预测。尽管该模型基于这一特定案例进行评估，但其设计尽可能保持通用性，从而可适用于多种基于图像的分类与回归任务。通过我们的框架，借助环境元数据的预训练过程，甚至可针对多目标问题训练模型。