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 and superior sim-to-real generalization. Our approach provides a promising solution for domain generalization in crop segmentation and has the potential to enhance a wide variety of precision agriculture applications.

翻译：近年来，精准农业逐渐推动农业生产向自动化流程转型，以支持田间管理相关工作。服务机器人通过部署自主智能体，在无需人工干预的情况下完成监测、喷洒、收割等任务，在这一变革中发挥着主导作用。为执行这些精细操作，移动机器人需要具备实时感知系统，以理解野外环境并识别目标对象。由于标注样本稀缺，泛化至新作物类型及环境条件对实际应用至关重要。本文针对作物分割问题展开研究，提出一种基于知识蒸馏增强域泛化的创新方法。在所提框架中，我们将多个源域独立训练模型的集成知识迁移至学生模型，使其能适应未知目标域。为验证该方法，我们构建了包含50000余样本的合成多域作物分割数据集，涵盖形态各异的植株、不同地形风格、天气状况及光照场景。实验结果表明，本方法在性能上显著超越现有技术，并展现出卓越的模拟到真实场景泛化能力。该研究为作物分割的域泛化问题提供了有效解决方案，有望推动精准农业多类应用的发展。