  Confinement during COVID-19 has caused serious effects on agriculture all
over the world. As one of the efficient solutions, mechanical
harvest/auto-harvest that is based on object detection and robotic harvester
becomes an urgent need. Within the auto-harvest system, robust few-shot object
detection model is one of the bottlenecks, since the system is required to deal
with new vegetable/fruit categories and the collection of large-scale annotated
datasets for all the novel categories is expensive. There are many few-shot
object detection models that were developed by the community. Yet whether they
could be employed directly for real life agricultural applications is still
questionable, as there is a context-gap between the commonly used training
datasets and the images collected in real life agricultural scenarios. To this
end, in this study, we present a novel cucumber dataset and propose two data
augmentation strategies that help to bridge the context-gap. Experimental
results show that 1) the state-of-the-art few-shot object detection model
performs poorly on the novel `cucumber' category; and 2) the proposed
augmentation strategies outperform the commonly used ones.
