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中的关键技术。大规模田间影像的高效获取与机器学习技术的结合，在表型分析、杂草识别、作物收割及病害防控等各类任务中展现出显著成效。本研究针对甜菜尾孢菌叶斑病（CLS）的病害严重度评分这一具体应用场景，提出了一种用于大规模植物特异性性状自动标注的机器学习框架。通过融合深度标签分布学习（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高效视觉Transformer的病害严重度评分模型，命名为SugarViT。本研究的创新之处在于将遥感数据与试验场地的环境参数相结合用于病害严重度预测。尽管该模型针对这一特定应用场景进行了评估，但其设计尽可能保持通用性，以适用于多种基于图像的分类与回归任务。通过我们的框架，甚至可以实现多目标问题的模型学习——我们通过基于环境元数据的预训练实验验证了这一点。