Agricultural robots have the prospect to enable more efficient and sustainable agricultural production of food, feed, and fiber. Perception of crops and weeds is a central component of agricultural robots that aim to monitor fields and assess the plants as well as their growth stage in an automatic manner. Semantic perception mostly relies on deep learning using supervised approaches, which require time and qualified workers to label fairly large amounts of data. In this paper, we look into the problem of reducing the amount of labels without compromising the final segmentation performance. For robots operating in the field, pre-training networks in a supervised way is already a popular method to reduce the number of required labeled images. We investigate the possibility of pre-training in a self-supervised fashion using data from the target domain. To better exploit this data, we propose a set of domain-specific augmentation strategies. We evaluate our pre-training on semantic segmentation and leaf instance segmentation, two important tasks in our domain. The experimental results suggest that pre-training with domain-specific data paired with our data augmentation strategy leads to superior performance compared to commonly used pre-trainings. Furthermore, the pre-trained networks obtain similar performance to the fully supervised with less labeled data.

翻译：农业机器人有望实现粮食、饲料和纤维生产的更高效与可持续。作物与杂草感知是农业机器人的核心组成部分，旨在自动监测田地并评估植物及其生长阶段。语义感知主要依赖基于监督式深度学习的途径，这需要时间与专业人员进行大量数据标注。本文探讨如何在保证最终分割性能的前提下减少标注量。针对田间作业的机器人，监督式网络预训练已是减少所需标注图像数量的常用方法。我们研究了利用目标领域数据进行自监督预训练的可行性。为更充分利用此类数据，提出一组领域特定增强策略。我们分别在语义分割与叶片实例分割这两项领域关键任务上评估预训练效果。实验结果表明，结合所提数据增强策略的领域特定数据预训练，其性能优于常用预训练方法。此外，预训练网络在较少标注数据条件下可达到与全监督方法相当的性能。