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任务中展现出成功应用。本研究提出一个用于大规模植物特异性性状自动标注的机器学习框架，以甜菜叶斑病（Cercospora Leaf Spot, CLS）的病害严重度评分为应用场景。通过引入深度标签分布学习（Deep Label Distribution Learning, DLDL）、专用损失函数及定制化模型架构，我们开发了一种基于视觉Transformer（Vision Transformer）的高效病害严重度评分模型，命名为SugarViT。本研究的一个创新点在于将遥感数据与实验地点的环境参数相结合用于病害严重度预测。尽管该模型在此特定应用场景下进行评估，但其设计保持尽可能通用，可适用于多种基于图像的分类与回归任务。借助我们的框架，甚至能够针对多目标问题学习模型，这一点通过基于环境元数据的预训练实验得到证明。