  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.
