Integrating machine learning and large language models to enhance risk assessment and priority management for plant biosecurity. Haoxiang Zhao a, Shanqing Yi a, Andy Sheppard b, Rieks van Klinken b, Nianwan Yang a c, Jue Wang d, Xiaoqing Xian a, Fanghao Wan a, Wanxue Liu

Source： Artificial Intelligence in Agriculture

Published： July 2026

DOI: 10.1016/j.aiia.2026.07.004

IF：16.1

Abstract： Global food security and biosecurity are continually threatened by the prevalence and spread of plant pests with the ever-accelerating rate of global trade. The continuous advancement of artificial intelligence, such as large language models (LLMs) and machine learning, provides novel insights into plant pest risk management in the food trade, whereas their practical effectiveness remains insufficiently understood. Here, by integrating an LLM with the retrieval-augmented generation method, a machine learning-based network analysis and ecological niche modeling, we constructed a risk management framework and a decision support system to assess the multistage risk of pests in the citrus trade, thereby supporting priority risk management. The introduction risk of citrus pests tended to increase from 2003 to 2018, with 243 introduction risk pathways and 98 risk nodes, mainly including scale insects and fruit flies. Our findings highlight the high risk of co-introduction and co-establishment of multiple citrus pests at important citrus planting areas in southern China, as well as the risk of spread into high-latitude regions under climate change. The decision support platform demonstrated reliable citrus pest risk management performance. Additionally, our study provides novel insights into multistage risk management of plant pests with various trade pathways.