# AgroCoT: A Chain-of-Thought Benchmark for Evaluating Reasoning in Vision-Language Models for Agriculture

arXiv: https://arxiv.org/abs/2511.23253 · HTML: https://mlsanity.com/paper/2511.23253/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 27 of its 63 references that are in the map.
- First posted on arXiv 28 Nov 2025.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)
- Multimodal LLMs / vision-language models / [Agricultural vision-language models](https://mlsanity.com/mllm/agricultural-vision-language-models/)

## Builds on (most cited first, 27 of 27)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts](https://mlsanity.com/paper/2310.02255/) (2023)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [Gemini: A Family of Highly Capable Multimodal Models](https://mlsanity.com/paper/2312.11805/) (2023)
- [DeepSeek-V3 Technical Report](https://mlsanity.com/paper/2412.19437/) (2024)
- [DeepSeek-VL: Towards Real-World Vision-Language Understanding](https://mlsanity.com/paper/2403.05525/) (2024)
- [GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning](https://mlsanity.com/paper/2507.01006/) (2025)
- [Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey](https://mlsanity.com/paper/2503.12605/) (2025)
- [TinyLLaVA: A Framework of Small-scale Large Multimodal Models](https://mlsanity.com/paper/2402.14289/) (2024)
- [Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark](https://mlsanity.com/paper/2501.05444/) (2025)
- [MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency](https://mlsanity.com/paper/2502.09621/) (2025)
- [A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges](https://mlsanity.com/paper/2501.02189/) (2025)
- [THINK-Bench: Evaluating Thinking Efficiency and Chain-of-Thought Quality of Large Reasoning Models](https://mlsanity.com/paper/2505.22113/) (2025)
- [RBench-V: A Primary Assessment for Visual Reasoning Models with Multi-modal Outputs](https://mlsanity.com/paper/2505.16770/) (2025)
- [Assessing GPT4-V on Structured Reasoning Tasks](https://mlsanity.com/paper/2312.11524/) (2023)
- [Agri-LLaVA: Knowledge-Infused Large Multimodal Assistant on Agricultural Pests and Diseases](https://mlsanity.com/paper/2412.02158/) (2024)
- [MIRAGE: A Benchmark for Multimodal Information-Seeking and Reasoning in Agricultural Expert-Guided Conversations](https://mlsanity.com/paper/2506.20100/) (2025)
- [AgriGPT-VL: Agricultural Vision-Language Understanding Suite](https://mlsanity.com/paper/2510.04002/) (2025)
- [Benchmarking Multimodal CoT Reward Model Stepwise by Visual Program](https://mlsanity.com/paper/2504.06606/) (2025)
- [Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind](https://mlsanity.com/paper/2505.12207/) (2025)
- [PRISM-Bench: A Benchmark of Puzzle-Based Visual Tasks with CoT Error Detection](https://mlsanity.com/paper/2510.23594/) (2025)

## Built on by (most cited first, 1 of 1)

- [AgroGround: Multi-Granularity Grounded Recognition in Agriculture](https://mlsanity.com/paper/2610.04425/) (2026)

## Abstract (arXiv)

Recent advancements in Vision-Language Models (VLMs) have significantly impacted various industries. In agriculture, these multimodal capabilities hold great promise for applications such as precision farming, crop monitoring, pest detection, and environmental sustainability. However, while several Visual Question Answering (VQA) datasets and benchmarks have been developed to assess VLM performance, they often fail to effectively evaluate the critical reasoning and problem-solving skills needed in complex agricultural contexts. To address this gap, we introduce AgroCoT, a VQA dataset that integrates Chain-of-Thought (CoT) reasoning, specifically designed to evaluate the reasoning capabilities of VLMs. With 4,759 carefully curated samples, AgroCoT provides a comprehensive and robust evaluation of reasoning abilities, particularly in zero-shot scenarios, focusing on the models' ability to engage in logical reasoning and effective problem-solving. Our evaluation of 30 representative VLMs, including both proprietary and open-source models, reveals a gap in their reasoning capabilities, which underscores the importance of incorporating CoT for assessments. Our dataset is available at https://huggingface.co/datasets/AgroCoT/AgroCoT.