  In the era of digitization, different actors in agriculture produce numerous
data. Such data contains already latent historical knowledge in the domain.
This knowledge enables us to precisely study natural hazards within global or
local aspects, and then improve the risk prevention tasks and augment the
yield, which helps to tackle the challenge of growing population and changing
alimentary habits. In particular, French Plants Health Bulletins (BSV, for its
name in French Bulletin de Sant{\'e} du V{\'e}g{\'e}tal) give information about
the development stages of phytosanitary risks in agricultural production.
However, they are written in natural language, thus, machines and human cannot
exploit them as efficiently as it could be. Natural language processing (NLP)
technologies aim to automatically process and analyze large amounts of natural
language data. Since the 2010s, with the increases in computational power and
parallelization, representation learning and deep learning methods became
widespread in NLP. Recent advancements Bidirectional Encoder Representations
from Transformers (BERT) inspire us to rethink of knowledge representation and
natural language understanding in plant health management domain. The goal in
this work is to propose a BERT-based approach to automatically classify the BSV
to make their data easily indexable. We sampled 200 BSV to finetune the
pretrained BERT language models and classify them as pest or/and disease and we
show preliminary results.
