preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202408.2157.v1 (registering DOI)

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 29 August 2024 / Approved: 29 August 2024 / Online: 29 August 2024 (11:33:08 CEST)

In modern agriculture, decision-making is deeply intertwined with data, yet essential agricultural knowledge remains encased within dense scientific texts such as journals, manuals, and free-text reports. This necessitates specialized search systems capable of distilling and delivering precise information in response to specific queries from agricultural users. This study introduces AgriQuery, a sophisticated agent designed to field natural language questions from the agricultural sector by exploring vast troves of scientific documents. A comprehensive review and analysis of farmers' information needs have been conducted, leading to the creation of an information retrieval test collection that includes actual queries, a vast array of scientific documents segmented into passages, and a set of ground truth relevance assessments that map these passages to corresponding queries. We explore and benchmark several information retrieval models, highlighting the efficacy of advanced neural ranking models in this context. AgriQuery's proposed deployment architecture features a client interface on the Telegram platform and a backend model on standard hardware. This test collection aims to catalyze further research into matching natural language queries with scientific text answers, with implications extending beyond agriculture.

information retrieval; neural ranking models,; natural language processing; data-driven decision making

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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