# Enhancing Agricultural Machinery Management through Advanced LLM Integration

arXiv: https://arxiv.org/abs/2407.20588 · HTML: https://mlsanity.com/paper/2407.20588/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 31 references that are in the map.
- First posted on arXiv 30 Jul 2024.

## Builds on (most cited first, 26 of 26)

- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Massive Activations in Large Language Models](https://mlsanity.com/paper/2402.17762/) (2024)
- [Tool Learning with Large Language Models: A Survey](https://mlsanity.com/paper/2405.17935/) (2024)
- [Evaluating Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2310.19736/) (2023)
- [Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey](https://mlsanity.com/paper/2402.17944/) (2024)
- [QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2309.14717/) (2023)
- [Visual In-Context Learning for Large Vision-Language Models](https://mlsanity.com/paper/2402.11574/) (2024)
- [Large Language Models Meet NLP: A Survey](https://mlsanity.com/paper/2405.12819/) (2024)
- [Large Language Models and Games: A Survey and Roadmap](https://mlsanity.com/paper/2402.18659/) (2024)
- [Eight Things to Know about Large Language Models](https://mlsanity.com/paper/2304.00612/) (2023)
- [Thread of Thought Unraveling Chaotic Contexts](https://mlsanity.com/paper/2311.08734/) (2023)
- [ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification](https://mlsanity.com/paper/2203.02225/) (2022)
- [EventBERT: A Pre-Trained Model for Event Correlation Reasoning](https://mlsanity.com/paper/2110.06533/) (2021)
- [Fine-Grained Distillation for Long Document Retrieval](https://mlsanity.com/paper/2212.10423/) (2022)
- [The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRs](https://mlsanity.com/paper/2303.12961/) (2023)
- [MemoryMamba: Memory-Augmented State Space Model for Defect Recognition](https://mlsanity.com/paper/2405.03673/) (2024)
- [Improving Cross-modal Alignment for Text-Guided Image Inpainting](https://mlsanity.com/paper/2301.11362/) (2023)
- [IGrow: A Smart Agriculture Solution to Autonomous Greenhouse Control](https://mlsanity.com/paper/2107.05464/) (2021)
- [Agricultural 4.0 Leveraging on Technological Solutions: Study for Smart Farming Sector](https://mlsanity.com/paper/2401.00814/) (2024)
- [Transforming Agriculture with Intelligent Data Management and Insights](https://mlsanity.com/paper/2401.13672/) (2023)
- [A multi-sensor robotic platform for ground mapping and estimation beyond the visible spectrum](https://mlsanity.com/paper/2104.05259/) (2021)
- [Harnessing Artificial Intelligence for Sustainable Agricultural Development in Africa: Opportunities, Challenges, and Impact](https://mlsanity.com/paper/2401.06171/) (2024)
- [Intelligent Agricultural Greenhouse Control System Based on Internet of Things and Machine Learning](https://mlsanity.com/paper/2402.09488/) (2024)
- [A Pilot Study of Smart Agricultural Irrigation using Unmanned Aerial Vehicles and IoT-Based Cloud System](https://mlsanity.com/paper/2101.01851/) (2021)
- [Deep learning, machine vision in agriculture in 2021](https://mlsanity.com/paper/2103.04893/) (2021)
- [Internet of Things (IoT) based Smart Agriculture Aiming to Achieve Sustainable Goals](https://mlsanity.com/paper/2206.06300/) (2022)

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

- [MAD-Fact: A Multi-Agent Debate Framework for Long-Form Factuality Evaluation in LLMs](https://mlsanity.com/paper/2510.22967/) (2025)

## Abstract (arXiv)

The integration of artificial intelligence into agricultural practices, specifically through Consultation on Intelligent Agricultural Machinery Management (CIAMM), has the potential to revolutionize efficiency and sustainability in farming. This paper introduces a novel approach that leverages large language models (LLMs), particularly GPT-4, combined with multi-round prompt engineering to enhance decision-making processes in agricultural machinery management. We systematically developed and refined prompts to guide the LLMs in generating precise and contextually relevant outputs. Our approach was evaluated using a manually curated dataset from various online sources, and performance was assessed with accuracy and GPT-4 Scores. Comparative experiments were conducted using LLama-2-70B, ChatGPT, and GPT-4 models, alongside baseline and state-of-the-art methods such as Chain of Thought (CoT) and Thought of Thought (ThoT). The results demonstrate that our method significantly outperforms these approaches, achieving higher accuracy and relevance in generated responses. This paper highlights the potential of advanced prompt engineering techniques in improving the robustness and applicability of AI in agricultural contexts.