AgriBench: A Hierarchical Agriculture Benchmark for Multimodal Large Language Models

• URL: http://arxiv.org/abs/2412.00465v2
• Date: Sat, 21 Dec 2024 16:18:42 GMT
• Title: AgriBench: A Hierarchical Agriculture Benchmark for Multimodal Large Language Models
• Authors: Yutong Zhou, Masahiro Ryo,
• Abstract summary: AgriBench is the first benchmark designed to evaluate MultiModal Large Language Models (MM-LLMs) for agriculture applications. We propose MM-LUCAS, a multimodal agriculture dataset that includes 1,784 landscape images, segmentation masks, depth maps, and detailed annotations. This work presents a groundbreaking perspective in advancing agriculture MM-LLMs and is still in progress, offering valuable insights for future developments and innovations in specific expert knowledge-based MM-LLMs.
• Score: 4.12825661607328
• License:
• Abstract: We introduce AgriBench, the first agriculture benchmark designed to evaluate MultiModal Large Language Models (MM-LLMs) for agriculture applications. To further address the agriculture knowledge-based dataset limitation problem, we propose MM-LUCAS, a multimodal agriculture dataset, that includes 1,784 landscape images, segmentation masks, depth maps, and detailed annotations (geographical location, country, date, land cover and land use taxonomic details, quality scores, aesthetic scores, etc), based on the Land Use/Cover Area Frame Survey (LUCAS) dataset, which contains comparable statistics on land use and land cover for the European Union (EU) territory. This work presents a groundbreaking perspective in advancing agriculture MM-LLMs and is still in progress, offering valuable insights for future developments and innovations in specific expert knowledge-based MM-LLMs.

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