mlsanityData to 14 Oct 2026

AgriBench: A Hierarchical Agriculture Benchmark for Multimodal Large Language Models

arXiv 2412.00465 ↗First posted 30 Nov 2024cs.CV cs.AIcited by 7

On the map: Multimodal LLMs / vision-language models → Agricultural vision-language models

Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Placed in Agricultural vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 24 of its 53 references that are in the map. First posted on arXiv 30 Nov 2024.

Builds on

24 of 53 references.

References · 24

  1. 2023GPT-4 Technical Reportcited by 18,396
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12 more
  1. 2023Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levelscited by 503
  2. 2024MM-LLMs: Recent Advances in MultiModal Large Language Modelscited by 278
  3. 2024Efficient Multimodal Learning from Data-centric Perspectivecited by 122
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  11. 2021Field Evaluations of A Deep Learning-based Intelligent Spraying Robot with Flow Control for Pear Orchardscited by 5
  12. 2023Crop identification using deep learning on LUCAS crop cover photoscited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 4.

7 papers.

  1. 2025AgMMU: A Comprehensive Agricultural Multimodal Understanding Benchmarkcited by 16
  2. 2025AgriGPT-VL: Agricultural Vision-Language Understanding Suitecited by 11
  3. 2026LeafNet: A Large-Scale Dataset and Comprehensive Benchmark for Foundational Vision-Language Understanding of Plant Diseasescited by 5
  4. 2025Towards Large Reasoning Models for Agriculturecited by 3
  5. 2026Multi-label Instance-level Generalised Visual Grounding in Agriculturecited by 2
  6. 2026AgroVG: A Large-Scale Multi-Source Benchmark for Agricultural Visual Groundingcited by 1
  7. 2026LUCAS-MEGA: A Large-Scale Multimodal Dataset for Representation Learning in Soil-Environment Systemscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026AgriChat: A Multimodal Large Language Model for Agriculture Image Understanding
  2. 2026AgroOmni: A Large-Scale Multi-view Agricultural Dataset for Cross-Scale Multimodal Reasoning
  3. 2024AgroGPT: Efficient Agricultural Vision-Language Model with Expert Tuning
  4. 2025AgriGPT-Omni: A Unified Speech-Vision-Text Framework for Multilingual Agricultural Intelligence
  5. 2024Harnessing Large Vision and Language Models in Agriculture: A Review
  6. 2025Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind
  7. 2025AgriGPT: a Large Language Model Ecosystem for Agriculture
  8. 2024Agri-LLaVA: Knowledge-Infused Large Multimodal Assistant on Agricultural Pests and Diseases