In recent years, precision agriculture has gradually oriented farming closer to automation processes to support all the activities related to field management. Service robotics plays a predominant role in this evolution by deploying autonomous agents that can navigate fields while performing tasks without human intervention, such as monitoring, spraying, and harvesting. To execute these precise actions, mobile robots need a real-time perception system that understands their surroundings and identifies their targets in the wild. Generalizing to new crops and environmental conditions is critical for practical applications, as labeled samples are rarely available. In this paper, we investigate the problem of crop segmentation and propose a novel approach to enhance domain generalization using knowledge distillation. In the proposed framework, we transfer knowledge from an ensemble of models individually trained on source domains to a student model that can adapt to unseen target domains. To evaluate the proposed method, we present a synthetic multi-domain dataset for crop segmentation containing plants of variegate shapes and covering different terrain styles, weather conditions, and light scenarios for more than 50,000 samples. We demonstrate significant improvements in performance over state-of-the-art methods. Our approach provides a promising solution for domain generalization in crop segmentation and has the potential to enhance precision agriculture applications.

翻译：近年来，精准农业逐步推动耕作过程向自动化方向发展，以支持田间管理的各项活动。服务机器人在这一变革中发挥着主导作用，通过部署自主智能体在无需人为干预的情况下执行监测、喷洒和收割等田间作业任务。为完成这些精准操作，移动机器人需要具备实时感知系统，能够理解野外环境并识别目标对象。在实际应用中，由于标注样本稀缺，对新作物类型及环境条件的泛化能力至关重要。本文针对作物分割问题展开研究，提出一种基于知识蒸馏的域泛化增强新方法。在所提出的框架中，我们将分别从多个源域训练得到的集成模型知识迁移至学生模型，使其能够适应未见过的目标域。为评估该方法，我们构建了一个包含5万余个样本的合成多域作物分割数据集，涵盖形态各异的植株、不同地形类型、天气条件及光照场景。实验结果表明，该方法相较于现有最优技术实现了显著性能提升。我们的方法为作物分割的域泛化问题提供了具有前景的解决方案，有望增强精准农业应用效能。