Mobile robots will play a crucial role in the transition towards sustainable agriculture. To autonomously and effectively monitor the state of plants, robots ought to be equipped with visual perception capabilities that are robust to the rapid changes that characterise agricultural settings. In this paper, we focus on the challenging task of segmenting grape bunches from images collected by mobile robots in vineyards. In this context, we present the first study that applies surgical fine-tuning to instance segmentation tasks. We show how selectively tuning only specific model layers can support the adaptation of pre-trained Deep Learning models to newly-collected grape images that introduce visual domain shifts, while also substantially reducing the number of tuned parameters.

翻译：移动机器人将在农业可持续发展的转型中发挥关键作用。为自主有效地监测植物状态，机器人需配备能适应农业环境快速变化的视觉感知能力。本文聚焦于移动机器人在葡萄园采集图像中分割葡萄串这一挑战性任务，首次将手术式微调应用于实例分割任务。研究表明，选择性调整特定模型层可支持预训练深度学习模型适应新采集的、引入视觉域偏移的葡萄图像，同时显著减少待调参数数量。