  An important application scenario of precision agriculture is detecting and
measuring crop health threats using sensors and data analysis techniques.
However, the textual data are still under-explored among the existing solutions
due to the lack of labelled data and fine-grained semantic resources. Recent
research suggests that the increasing connectivity of farmers and the emergence
of online farming communities make social media like Twitter a participatory
platform for detecting unfamiliar plant health events if we can extract
essential information from unstructured textual data. ChouBERT is a French
pre-trained language model that can identify Tweets concerning observations of
plant health issues with generalizability on unseen natural hazards. This paper
tackles the lack of labelled data by further studying ChouBERT's know-how on
token-level annotation tasks over small labeled sets.
