He, WanruiMcGuinness, BenjaminHolmes, GeoffreyFletcher, DaleDuke, MikeAu, Chi KitWilliams, Henry A. M.Smith, DavidLim, Shen Hin2026-09-102026-09-102025He, W., McGuinness, B., Holmes, G., Fletcher, D., Duke, M., Au, C. K., Williams, H., Smith, D., & Lim, S. H. (2025). Exploring prompt engineering for grapevine pruning decision making with limited data. Australasian Conference on Robotics and Automation, ACRA.1448-2053https://hdl.handle.net/10289/18633To address the challenges posed by the shortage of agricultural labour, this paper explores the use of Prompt Engineering (PE) to guide Large Language Models (LLMs) for grapevine pruning decision-making. Based on real grapevine images and structured data, five prompt templates were designed and tested on four mainstream LLMs: Gemma-3, Llama-4, Gemini 2.5 Pro, and GPT-o3. Experimental results show that some models excel in consistency—GPT-o3 achieved full consistency (1.0) under Prompt 3, and Gemma-3 performed well under Prompts 2 and 3 (0.98 and 0.96). However, inter-model agreement remained low (Fleiss’ Kappa < 0.07) across all conditions. These findings confirm the feasibility of using PE to simulate expert pruning in data-scarce environments and offer insights for building scalable, interpretable decision-support systems in agriculture.enThis is a conference presentation from the proceedings of the Australasian Conference on Robotics and Automation (ACRA 2025). © 2025 The authors.computer scienceConference Contribution