Surgical fine-tuning for Grape Bunch Segmentation under Visual Domain Shifts

TL;DR

This paper introduces a surgical fine-tuning approach for grape bunch segmentation in vineyard images, enabling robust adaptation of pre-trained models to visual domain shifts with fewer tuned parameters.

Contribution

It is the first study to apply surgical fine-tuning to instance segmentation, improving model adaptation efficiency in agricultural robotics.

Findings

Selective layer tuning supports domain adaptation

Reduces number of tuned parameters

Enhances segmentation robustness in vineyards

Abstract

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.