LLM-Driven 3D Scene Generation of Agricultural Simulation Environments
Faculty of Applied Engineering – Dept. of Electronics and ICT, University of Antwerp（应用工程学院——电子与信息通信技术系，安特卫普大学）；Flanders Make Strategic Research Centre（弗拉芒制造战略研究中心）
Arafa Yoncalik, Wouter Jansen, Nico Huebel, Mohammad Hasan Rahmani, Jan Steckel
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Accepted at IEEE Conference on Artificial Intelligence 2026
Procedural generation techniques in 3D rendering engines have revolutionized the creation of complex environments, reducing reliance on manual design. Recent approaches using Large Language Models (LLMs) for 3D scene generation show promise but often lack domain-specific reasoning, verification mechanisms, and modular design. These limitations lead to reduced control and poor scalability. This paper investigates the use of LLMs to generate agricultural synthetic simulation environments from natural language prompts, specifically to address the limitations of lacking domain-specific reasoning, verification mechanisms, and modular design. A modular multi-LLM pipeline was developed, integrating 3D asset retrieval, domain knowledge injection, and code generation for the Unreal rendering engine using its API. This results in a 3D environment with realistic planting layouts and environmental context, all based on the input prompt and the domain knowledge. To enhance accuracy and scalability, the system employs a hybrid strategy combining LLM optimization techniques such as few-shot prompting, Retrieval-Augmented Generation (RAG), finetuning, and validation. Unlike monolithic models, the modular architecture enables structured data handling, intermediate verification, and flexible expansion. The system was evaluated using structured prompts and semantic accuracy metrics. A user study assessed realism and familiarity against real-world images, while an expert comparison demonstrated significant time savings over manual scene design. The results confirm the effectiveness of multi-LLM pipelines in automating domain-specific 3D scene generation with improved reliability and precision. Future work will explore expanding the asset hierarchy, incorporating real-time generation, and adapting the pipeline to other simulation domains beyond agriculture.
Leveraging Synthetic Data for Question Answering with Multilingual LLMs in the Agricultural Domain
CSIR-Central Scientific Instruments Organisation, India（印度CSIR-中央科学仪器组织）；Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, U.P, India（印度科学与创新研究院（AcSIR））；School of Mathematical and Computational Sciences, Massey University, New Zealand（梅西大学数学与计算科学学院）
ShizishanGPT: An Agricultural Large Language Model Integrating Tools and Resources
College of Informatics, Huazhong Agricultural University（华中农业大学信息学院）；Industrial AI Research Centre, University of South Australia（南澳大学工业人工智能中心）；College of Plant Science and Technology, Huazhong Agricultural University（华中农业大学植物科学技术学院）
AgriRegion: Region-Aware Retrieval for High-Fidelity Agricultural Advice
North Carolina A\&T State University（北卡罗来纳A&T州立大学）；The University of Alabama（阿拉巴马大学）；North Carolina Agricultural and Technical State University（北卡罗来纳农业与技术州立大学）；Amazon AWS（亚马逊AWS）
Agri-CPJ: A Training-Free Explainable Framework for Agricultural Pest Diagnosis Using Caption-Prompt-Judge and LLM-as-a-Judge
Business School, Shandong University of Technology（山东理工大学商学院）；Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）；Guangdong Institute of Intelligent Science and Technology（广东智能科学与技术研究院）；Macau Millennium College（澳门 millennium 学院）；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）
A Multimodal Conversational Assistant for the Characterization of Agricultural Plots from Geospatial Open Data
Dept. AI \& Data Analytics CTIC Technology Center Gijón, Spain；Dept. IT CTIC Technology Center Gijón, Spain；Eng. University of Oviedo Gijón, Spain；Dept. R \& D CTIC Technology Center Gijón, Spain
AgroGPT: Efficient Agricultural Vision-Language Model with Expert Tuning
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Georgia Institute of Technology（佐治亚理工学院）；Johns Hopkins University（约翰斯·霍普金斯大学）
AgroLLM: Connecting Farmers and Agricultural Practices through Large Language Models for Enhanced Knowledge Transfer and Practical Application
Pittsburg State University（匹兹堡州立大学）；Oxford Brookes University（牛津布鲁克斯大学）；Kelce College of Business, Pittsburg State University（匹兹堡州立大学凯尔斯商学院）；MSK Cancer Center（纪念斯隆-凯特琳癌症中心）
Agent-Based Post-Hoc Correction of Agricultural Yield Forecasts
School of Natural and Computing Sciences（自然与计算科学学院）；University of Aberdeen（阿伯丁大学）；School of Computing Sciences（计算科学学院）；University of East Anglia（东安格利亚大学）；Department of Physics and Technology（物理与技术系）；UiT The Arctic University of Norway（北欧大学（UiT））
AgriGPT: a Large Language Model Ecosystem for Agriculture
College of Computer Science and Technology, Zhejiang University, China（浙江大学计算机科学与技术学院）；College of Software Technology, Zhejiang University, China（浙江大学软件学院）；School of Geological Engineering and Geomatics, Chang’an University, China（长安大学地质工程与测绘学院）；College of Biosystems Engineering and Food Science, Zhejiang University, China（浙江大学生物系统工程与食品科学学院）；Faculty of Computer and Information Sciences, Hosei University, Japan（立命馆大学计算机与信息科学系）
Traj-LLM: A New Exploration for Empowering Trajectory Prediction with Pre-trained Large Language Models
Beihang University（北京航空航天大学）；Research Institute for Frontier Science, Beihang University（北京航空航天大学前沿科学研究院）；Beijing University of Technology（北京工业大学）；College of Metropolitan Transportation, Beijing University of Technology（北京工业大学城市交通学院）
Context Matters! Relaxing Goals with LLMs for Feasible 3D Scene Planning
Department of Computer, Automation and Management Engineering, Sapienza University of Rome（Sapienza大学计算机、自动化与管理工程系）；Department of Electronics and Automation, Mohamed Khider University of Biskra（Mohamed Khider大学电子与自动化系）；International University of Rome UNINT, Rome（罗马国际大学UNINT）
PerceptTwin: Semantic Scene Reconstruction for Iterative LLM Planning and Verification
Department of Computer Science and Operations Research, Université de Montréal（蒙特利尔大学计算机科学与运筹学系）；Mila - Quebec AI Institute（魁北克人工智能研究所）；CIFAR AI Chair（CIFAR人工智能主席）
AGSPNet: A framework for parcel-scale crop fine-grained semantic change detection from UAV high-resolution imagery with agricultural geographic scene constraints
Hunan University of Science and Technology（湖南科技大学）
LD-Scene: LLM-Guided Diffusion for Controllable Generation of Adversarial Safety-Critical Driving Scenarios
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）
Learning to Reason over Scene Graphs: A Case Study of Finetuning GPT-2 into a Robot Language Model for Grounded Task Planning
Technische Universität Darmstadt（达姆施塔特工业大学）；Center for Mind, Brain and Behavior, Uni. Marburg and JLU Giessen（马尔堡大学与吉森尤斯图斯·李比希大学心智、大脑与行为研究中心）；Amazon Alexa（亚马逊Alexa）
NavRAG: Generating User Demand Instructions for Embodied Navigation through Retrieval-Augmented LLM
School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；College of Information Science and Technology, Beijing University of Chemical Technology（北京化工大学信息科学与技术学院）
