In the era of sustainable smart agriculture, a massive amount of agricultural news text is being posted on the Internet, in which massive agricultural knowledge has been accumulated. In this context, it is urgent to explore effective text classification techniques for users to access the required agricultural knowledge with high efficiency. Mainstream deep learning approaches employing fine-tuning strategies on pre-trained language models (PLMs), have demonstrated remarkable performance gains over the past few years. Nonetheless, these methods still face many drawbacks that are complex to solve, including: 1. Limited agricultural training data due to the expensive-cost and labour-intensive annotation; 2. Poor domain transferability, especially of cross-linguistic ability; 3. Complex and expensive large models deployment.Inspired by the extraordinary success brought by the recent ChatGPT (e.g. GPT-3.5, GPT-4), in this work, we systematically investigate and explore the capability and utilization of ChatGPT applying to the agricultural informatization field. ....(shown in article).... Code has been released on Github https://github.com/albert-jin/agricultural_textual_classification_ChatGPT.

翻译：在可持续智慧农业时代，互联网上涌现出海量农业新闻文本，其中积累了丰富的农业知识。在此背景下，亟需探索高效的文本分类技术，以帮助用户快速获取所需的农业知识。采用预训练语言模型（PLMs）微调策略的主流深度学习方法，在过去数年间展现出显著的性能提升。然而，这些方法仍面临诸多难以解决的缺陷，包括：1. 农业训练数据有限（因标注成本高昂且劳动密集）；2. 领域迁移能力差，尤其是跨语言能力不足；3. 大模型部署复杂且成本高昂。受近期ChatGPT（如GPT-3.5、GPT-4）带来的非凡成功启发，本研究系统性地探讨并挖掘了ChatGPT在农业信息化领域的应用能力与潜力……（详见正文）……代码已发布至GitHub：https://github.com/albert-jin/agricultural_textual_classification_ChatGPT。