mlsanityData to 7 Oct 2026

AgroBench: Vision-Language Model Benchmark in Agriculture

arXiv 2507.20519 ↗First posted 28 Jul 2025cs.CVcited by 19

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

Abstract (arXiv)

Precise automated understanding of agricultural tasks such as disease identification is essential for sustainable crop production. Recent advances in vision-language models (VLMs) are expected to further expand the range of agricultural tasks by facilitating human-model interaction through easy, text-based communication. Here, we introduce AgroBench (Agronomist AI Benchmark), a benchmark for evaluating VLM models across seven agricultural topics, covering key areas in agricultural engineering and relevant to real-world farming. Unlike recent agricultural VLM benchmarks, AgroBench is annotated by expert agronomists. Our AgroBench covers a state-of-the-art range of categories, including 203 crop categories and 682 disease categories, to thoroughly evaluate VLM capabilities. In our evaluation on AgroBench, we reveal that VLMs have room for improvement in fine-grained identification tasks. Notably, in weed identification, most open-source VLMs perform close to random. With our wide range of topics and expert-annotated categories, we analyze the types of errors made by VLMs and suggest potential pathways for future VLM development. Our dataset and code are available at https://dahlian00.github.io/AgroBenchPage/ .

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Placed in Agricultural vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 32 of its 52 references that are in the map. First posted on arXiv 28 Jul 2025.

Builds on

32 of 52 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024AgroGPT: Efficient Agricultural Vision-Language Model with Expert Tuning10 Oct

Other references · 31

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2023Visual Instruction Tuningcited by 7,792
  3. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,069
  4. 2023Improved Baselines with Visual Instruction Tuningcited by 4,362
  5. 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,337
  6. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,310
  7. 2023Qwen Technical Reportcited by 3,273
  8. 2023MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGIcited by 2,044
  9. 2022ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoningcited by 1,449
  10. 2023MVBench: A Comprehensive Multi-modal Video Understanding Benchmarkcited by 1,086
  11. 2024Are We on the Right Way for Evaluating Large Vision-Language Models?cited by 893
  12. 2022PaLI: A Jointly-Scaled Multilingual Language-Image Modelcited by 762
19 more
  1. 2024Emu3: Next-Token Prediction is All You Needcited by 761
  2. 2023CogVLM: Visual Expert for Pretrained Language Modelscited by 704
  3. 2024MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmarkcited by 494
  4. 2023Generative Multimodal Models are In-Context Learnerscited by 446
  5. 2019PlotQA: Reasoning over Scientific Plotscited by 317
  6. 2016Using Deep Learning for Image-Based Plant Disease Detectioncited by 216
  7. 2023Emu: Generative Pretraining in Multimodalitycited by 156
  8. 2023Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Modelscited by 107
  9. 2018DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learningcited by 69
  10. 2019PlantDoc: A Dataset for Visual Plant Disease Detectioncited by 65
  11. 2024MultiMedEval: A Benchmark and a Toolkit for Evaluating Medical Vision-Language Modelscited by 28
  12. 2024Benchmarking In-the-wild Multimodal Disease Recognition and A Versatile Baselinecited by 19
  13. 2024Agri-LLaVA: Knowledge-Infused Large Multimodal Assistant on Agricultural Pests and Diseasescited by 14
  14. 2025A Multimodal Benchmark Dataset and Model for Crop Disease Diagnosiscited by 14
  15. 2025TimberVision: A Multi-Task Dataset and Framework for Log-Component Segmentation and Tracking in Autonomous Forestry Operationscited by 6
  16. 2023Crop identification using deep learning on LUCAS crop cover photoscited by 5
  17. 2024SBS Figures: Pre-training Figure QA from Stage-by-Stage Synthesized Imagescited by 5
  18. 2022Paddy Doctor: A Visual Image Dataset for Automated Paddy Disease Classification and Benchmarkingcited by 4
  19. 2024Image Classification for CSSVD Detection in Cacao Plantscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 14.

19 papers.

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  1. 2025AgriGPT-VL: Agricultural Vision-Language Understanding Suitecited by 11
  2. 2025AgriGPT-Omni: A Unified Speech-Vision-Text Framework for Multilingual Agricultural Intelligencecited by 4
  3. 2026LeafNet: A Large-Scale Dataset and Comprehensive Benchmark for Foundational Vision-Language Understanding of Plant Diseasescited by 4
  4. 2025PlantExpertVQA: A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Sciencecited by 3
  5. 2025Are vision-language models ready to zero-shot replace supervised classification models in agriculture?cited by 2
  6. 2025CPJ: Explainable Agricultural Pest Diagnosis via Caption-Prompt-Judge with LLM-Judged Refinementcited by 2
  7. 2026Benchmarking PhD-Level Coding in 3D Geometric Computer Visioncited by 2
  8. 2026Multi-label Instance-level Generalised Visual Grounding in Agriculturecited by 1
  9. 2026Using Vision Language Foundation Models to Generate Plant Simulation Configurations via In-Context Learningcited by 0
  10. 2026AgroOmni: A Large-Scale Multi-view Agricultural Dataset for Cross-Scale Multimodal Reasoningcited by 0
  11. 2026Agri-CPJ: A Training-Free Explainable Framework for Agricultural Pest Diagnosis Using Caption-Prompt-Judge and LLM-as-a-Judgecited by 0
  12. 2026CropVLM: A Domain-Adapted Vision-Language Model for Open-Set Crop Analysiscited by 0
7 more
  1. 2026AgroVG: A Large-Scale Multi-Source Benchmark for Agricultural Visual Groundingcited by 0
  2. 2026AgroTools: A Benchmark for Tool-Augmented Multimodal Agents in Agriculturecited by 0
  3. 2026Sci-Rho: A Multilingual Visually-Grounded Symbolic Benchmark for STEM Problemscited by 0
  4. 2026Benchmarking Vision-Language Models for Microscopic Plant Image Understandingcited by 0
  5. 2026Vision-language models know more about agriculture than they show and rubric-grounded verifications close the gapcited by 0
  6. 2026From Vision to Harvest: Benchmarking Vision-Language Models for Multi-Arm Robotic Fruit Harvestingcited by 0
  7. 2026BananaVLM: A Domain-Adapted Vision Language Model for Banana Crop Disease Diagnosiscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025AgroCoT: A Chain-of-Thought Benchmark for Evaluating Reasoning in Vision-Language Models for Agriculture
  2. 2026AgriChain Visually Grounded Expert Verified Reasoning for Interpretable Agricultural Vision Language Models
  3. 2026AgriChat: A Multimodal Large Language Model for Agriculture Image Understanding
  4. 2025AgMMU: A Comprehensive Agricultural Multimodal Understanding Benchmark
  5. 2024Leveraging Vision Language Models for Specialized Agricultural Tasks
  6. 2024Harnessing Large Vision and Language Models in Agriculture: A Review
  7. 2025Agro-Consensus: Semantic Self-Consistency in Vision-Language Models for Crop Disease Management in Developing Countries
  8. 2026From UAV Imagery to Agronomic Reasoning: A Multimodal LLM Benchmark for Plant Phenotyping