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.

精准农业应用领域中，使用传感器和数据分析技术检测和测量作物健康威胁是一个重要的应用场景。然而，由于缺乏标注数据和细粒度的语义资源，文本数据在现有解决方案中仍未得到充分探索。最近的研究表明，农民的日益连接性和在线农业社区的出现使得类似 Twitter 的社交媒体成为检测陌生植物健康事件的参与平台，只要我们能从非结构化文本数据中提取关键信息。ChouBERT 是法语预训练语言模型，能够准确识别涉及观察植物健康问题的推文，并且能推广到未见过的自然灾害。本文通过进一步研究 ChouBERT 在小规模标记数据上进行的令牌级注释任务，解决了标注数据的不足问题。