# ShizishanGPT: An Agricultural Large Language Model Integrating Tools and Resources

arXiv: https://arxiv.org/abs/2409.13537 · HTML: https://mlsanity.com/paper/2409.13537/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 22 references that are in the map.
- First posted on arXiv 20 Sep 2024.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023)
- [LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples](https://mlsanity.com/paper/2310.01469/) (2023)
- [RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture](https://mlsanity.com/paper/2401.08406/) (2024)
- [Don't Forget to Connect! Improving RAG with Graph-based Reranking](https://mlsanity.com/paper/2405.18414/) (2024)
- [AmadeusGPT: a natural language interface for interactive animal behavioral analysis](https://mlsanity.com/paper/2307.04858/) (2023)
- [GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models](https://mlsanity.com/paper/2310.06225/) (2023)

## Built on by (most cited first, 9 of 9)

- [PhenoAssistant: A Conversational Multi-Agent AI System for Automated Plant Phenotyping](https://mlsanity.com/paper/2504.19818/) (2025)
- [Towards Large Reasoning Models for Agriculture](https://mlsanity.com/paper/2505.19259/) (2025)
- [Dynamic Orchestration of Multi-Agent System for Real-World Multi-Image Agricultural VQA](https://mlsanity.com/paper/2509.24350/) (2025)
- [AgriLens: Semantic Retrieval in Agricultural Texts Using Topic Modeling and Language Models](https://mlsanity.com/paper/2601.08283/) (2026)
- [Towards AI Evaluation in Domain-Specific RAG Systems: The AgriHubi Case Study](https://mlsanity.com/paper/2602.02208/) (2026)
- [AgroOmni: A Large-Scale Multi-view Agricultural Dataset for Cross-Scale Multimodal Reasoning](https://mlsanity.com/paper/2603.14342/) (2026)
- [AgriIR: A Scalable Framework for Domain-Specific Knowledge Retrieval](https://mlsanity.com/paper/2604.16353/) (2026)
- [Fine-Tuning General-Purpose Large Language Models for Agricultural Applications:A Reproducible Framework and Evaluation Protocol Based on Qwen3-8B](https://mlsanity.com/paper/2606.28992/) (2026)
- [Agri-SAGE: Simulation-Grounded Multi-Agent LLM for Context-Aware Agricultural Advisory Generation](https://mlsanity.com/paper/2607.00454/) (2026)

## Abstract (arXiv)

Recent developments in large language models (LLMs) have led to significant improvements in intelligent dialogue systems'ability to handle complex inquiries. However, current LLMs still exhibit limitations in specialized domain knowledge, particularly in technical fields such as agriculture. To address this problem, we propose ShizishanGPT, an intelligent question answering system for agriculture based on the Retrieval Augmented Generation (RAG) framework and agent architecture. ShizishanGPT consists of five key modules: including a generic GPT-4 based module for answering general questions; a search engine module that compensates for the problem that the large language model's own knowledge cannot be updated in a timely manner; an agricultural knowledge graph module for providing domain facts; a retrieval module which uses RAG to supplement domain knowledge; and an agricultural agent module, which invokes specialized models for crop phenotype prediction, gene expression analysis, and so on. We evaluated the ShizishanGPT using a dataset containing 100 agricultural questions specially designed for this study. The experimental results show that the tool significantly outperforms general LLMs as it provides more accurate and detailed answers due to its modular design and integration of different domain knowledge sources. Our source code, dataset, and model weights are publicly available at https://github.com/Zaiwen/CropGPT.