Login | Request Account (DAF staff only)

AgAsk: an agent to help answer farmer’s questions from scientific documents

Share this record

Add to FacebookAdd to LinkedinAdd to XAdd to WechatAdd to Microsoft_teamsAdd to WhatsappAdd to Any

Export this record

View Altmetrics

Koopman, B., Mourad, A., Li, H., Vegt, A. v. d., Zhuang, S., Gibson, S., Dang, Y., Lawrence, D. and Zuccon, G. (2023) AgAsk: an agent to help answer farmer’s questions from scientific documents. International Journal on Digital Libraries . ISSN 1432-1300


Article Link: https://doi.org/10.1007/s00799-023-00369-y


Decisions in agriculture are increasingly data-driven. However, valuable agricultural knowledge is often locked away in free-text reports, manuals and journal articles. Specialised search systems are needed that can mine agricultural information to provide relevant answers to users’ questions. This paper presents AgAsk—an agent able to answer natural language agriculture questions by mining scientific documents. We carefully survey and analyse farmers’ information needs. On the basis of these needs, we release an information retrieval test collection comprising real questions, a large collection of scientific documents split in passages, and ground truth relevance assessments indicating which passages are relevant to each question. We implement and evaluate a number of information retrieval models to answer farmers questions, including two state-of-the-art neural ranking models. We show that neural rankers are highly effective at matching passages to questions in this context. Finally, we propose a deployment architecture for AgAsk that includes a client based on the Telegram messaging platform and retrieval model deployed on commodity hardware. The test collection we provide is intended to stimulate more research in methods to match natural language to answers in scientific documents. While the retrieval models were evaluated in the agriculture domain, they are generalisable and of interest to others working on similar problems. The test collection is available at: https://github.com/ielab/agvaluate.

Item Type:Article
Corporate Creators:Department of Agriculture and Fisheries, Queensland
Business groups:Crop and Food Science
Additional Information:The test collection is available at: https://github.com/ielab/agvaluate
Keywords:Information retrieval Professional search Domain-specific search Agriculture Passage retrieval
Subjects:Agriculture > Agriculture (General) > Documents and other collections
Agriculture > Agriculture (General) > Farm economics. Farm management. Agricultural mathematics
Technology > Technology (General)
Bibliography. Library Science. Information Resources > Information resources > Electronic information resources
Live Archive:30 Aug 2023 00:20
Last Modified:04 Sep 2023 04:45

Repository Staff Only: item control page


Downloads per month over past year

View more statistics