# Soft prompt-tuning for plant pest and disease classification from colloquial descriptions

## TL;DR

This paper introduces a method to classify plant pests and diseases from informal farmer descriptions using soft prompt-tuning, improving accuracy over existing models.

## Contribution

A novel soft prompt-tuning approach that bridges informal language and formal agricultural knowledge for pest and disease classification.

## Key findings

- The proposed method outperforms state-of-the-art models in classifying plant pests and diseases from colloquial inputs.
- Semantic alignment through knowledge enrichment improves model performance on informal descriptions.
- Soft prompt-tuning with an external knowledge verbalizer enhances classification accuracy in agricultural contexts.

## Abstract

The precise identification of plant pests and diseases plays a crucial role in preserving crop health and optimizing agricultural productivity. In practice, however, farmers frequently report symptoms in informal, everyday language. Traditional intelligent farming assistants are built upon domain-specific classification frameworks that depend on formal terminologies and structured symptom inputs, leading to subpar performance when faced with natural, unstructured farmer descriptions. To address this issue, we propose an innovative approach that classifies plant pests and diseases from colloquial symptom reports by leveraging soft prompt-tuning. Initially, we utilize Pretrained Language Models (PLMs) to conduct named entity recognition and retrieve domain-specific knowledge to enrich the input. Notably, this knowledge enrichment process introduces a kind of semantic alignment between the colloquial input and the acquired knowledge, enabling the model to better align informal expressions with formal agricultural concepts. Next, we apply a soft prompt-tuning strategy coupled with an external knowledge enhanced verbalizer for the classification task. The experimental results demonstrate that the proposed method outperforms baseline approaches, including state-of-the-art(SOTA) large language models (LLMs), in classifying plant pests and diseases from informal farmer descriptions. These results highlight the potential of prompt-tuning methods in bridging the gap between informal descriptions and expert knowledge, offering practical implications for the development of more accessible and intelligent agricultural support systems.