  This paper describes a novel method of training a semantic segmentation model
for scene recognition of agricultural mobile robots exploiting publicly
available datasets of outdoor scenes that are different from the target
greenhouse environments. Semantic segmentation models require abundant labels
given by tedious manual annotation. A method to work around it is unsupervised
domain adaptation (UDA) that transfers knowledge from labeled source datasets
to unlabeled target datasets. However, the effectiveness of existing methods is
not well studied in adaptation between heterogeneous environments, such as
urban scenes and greenhouses. In this paper, we propose a method to train a
semantic segmentation model for greenhouse images without manually labeled
datasets of greenhouse images. The core of our idea is to use multiple rich
image datasets of different environments with segmentation labels to generate
pseudo-labels for the target images to effectively transfer the knowledge from
multiple sources and realize a precise training of semantic segmentation. Along
with the pseudo-label generation, we introduce state-of-the-art methods to deal
with noise in the pseudo-labels to further improve the performance. We
demonstrate in experiments with multiple greenhouse datasets that our proposed
method improves the performance compared to the single-source baselines and an
existing approach.
