22 citations · 29 across the 7 of their papers we have counts for
7 papers
DeMMO: Longitudinal and Cross-Disease Modelling of Digital Mobility Outcomes via Multi-Task Learning
Menghui Zhou, Zhipeng Yuan, Vitaveska Lanfranchi +1
Digital mobility outcomes (DMOs) derived from wearable sensors characterise mobility in daily life and offer a promising means of monitoring disease progression. However, existing…
Pezego-HITL: A policy-grounded large language model architecture for agricultural extension in Ghana
Shunbao Li, Zhipeng Yuan, Amoako Ofori +5
Large language models are increasingly deployed in agricultural decision-support settings, yet high-stakes crop protection in smallholder agriculture requires more than output-qual…
PestMA: LLM-based Multi-Agent System for Informed Pest Management
Hongrui Shi, Shunbao Li, Zhipeng Yuan +1
Effective pest management is complex due to the need for accurate, context-specific decisions. Recent advancements in large language models (LLMs) open new possibilities for addres…
Exploring the Feasibility of Deep Learning Models for Long-term Disease Prediction: A Case Study for Wheat Yellow Rust in England
Zhipeng Yuan, Yu Zhang, Gaoshan Bi +1
Wheat yellow rust, caused by the fungus Puccinia striiformis, is a critical disease affecting wheat crops across Britain, leading to significant yield losses and economic consequen…
Quantifying Nematodes through Images: Datasets, Models, and Baselines of Deep Learning
Zhipeng Yuan, Nasamu Musa, Katarzyna Dybal +3
Every year, plant parasitic nematodes, one of the major groups of plant pathogens, cause a significant loss of crops worldwide. To mitigate crop yield losses caused by nematodes, a…
GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture
Shanglong Yang, Zhipeng Yuan, Shunbao Li +3
In the rapidly evolving field of artificial intelligence (AI), the application of large language models (LLMs) in agriculture, particularly in pest management, remains nascent. We…
Embedding-based Retrieval with LLM for Effective Agriculture Information Extracting from Unstructured Data
Ruoling Peng, Kang Liu, Po Yang +2
Pest identification is a crucial aspect of pest control in agriculture. However, most farmers are not capable of accurately identifying pests in the field, and there is a limited n…