AgriBench: A Hierarchical Agriculture Benchmark for Multimodal Large Language Models
This addresses the problem of evaluating agriculture applications for multimodal large language models, though it appears incremental as it adapts existing data to a new domain.
The authors tackled the lack of agriculture-specific benchmarks for multimodal large language models by introducing AgriBench, the first such benchmark, and MM-LUCAS, a multimodal agriculture dataset with 1,784 landscape images and detailed annotations based on EU land use data.
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.