# Fine-tuning BERT-based models for Plant Health Bulletin Classification

arXiv: https://arxiv.org/abs/2102.00838 · HTML: https://mlsanity.com/paper/2102.00838/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 29 Jan 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [FlauBERT: Unsupervised Language Model Pre-training for French](https://mlsanity.com/paper/1912.05372/) (2019)
- [word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method](https://mlsanity.com/paper/1402.3722/) (2014)
- [Open Data Platform for Knowledge Access in Plant Health Domain : VESPA Mining](https://mlsanity.com/paper/1504.06077/) (2015)

## Built on by (most cited first, 1 of 1)

- [ChatAgri: Exploring Potentials of ChatGPT on Cross-linguistic Agricultural Text Classification](https://mlsanity.com/paper/2305.15024/) (2023)

## Abstract (arXiv)

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'eg'etal) 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.