AgriChat: A Multimodal Large Language Model for Agriculture Image Understanding

• URL: http://arxiv.org/abs/2603.16934v1
• Date: Sat, 14 Mar 2026 11:49:05 GMT
• Title: AgriChat: A Multimodal Large Language Model for Agriculture Image Understanding
• Abstract summary: We present AgriChat, a specialized MLLM that presents broad knowledge across thousands of agricultural classes and provides detailed agricultural assessments with explanations.<n>The results validate that preserving visual detail combined with web-verified knowledge constitutes a reliable pathway toward robust and trustworthy agricultural AI.
• Score: 13.665861251747144
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The deployment of Multimodal Large Language Models (MLLMs) in agriculture is currently stalled by a critical trade-off: the existing literature lacks the large-scale agricultural datasets required for robust model development and evaluation, while current state-of-the-art models lack the verified domain expertise necessary to reason across diverse taxonomies. To address these challenges, we propose the Vision-to-Verified-Knowledge (V2VK) pipeline, a novel generative AI-driven annotation framework that integrates visual captioning with web-augmented scientific retrieval to autonomously generate the AgriMM benchmark, effectively eliminating biological hallucinations by grounding training data in verified phytopathological literature. The AgriMM benchmark contains over 3,000 agricultural classes and more than 607k VQAs spanning multiple tasks, including fine-grained plant species identification, plant disease symptom recognition, crop counting, and ripeness assessment. Leveraging this verifiable data, we present AgriChat, a specialized MLLM that presents broad knowledge across thousands of agricultural classes and provides detailed agricultural assessments with extensive explanations. Extensive evaluation across diverse tasks, datasets, and evaluation conditions reveals both the capabilities and limitations of current agricultural MLLMs, while demonstrating AgriChat's superior performance over other open-source models, including internal and external benchmarks. The results validate that preserving visual detail combined with web-verified knowledge constitutes a reliable pathway toward robust and trustworthy agricultural AI. The code and dataset are publicly available at https://github.com/boudiafA/AgriChat .

Related papers

• FORGE: Fine-grained Multimodal Evaluation for Manufacturing Scenarios [58.34124792457706]
  The manufacturing sector is increasingly adopting Multimodal Large Language Models (MLLMs) to transition from simple perception to autonomous execution.<n>Progress is hindered by data scarcity and a lack of fine-grained domain semantics in existing datasets.<n>We first construct a high-quality dataset that combines realworld 2D images and 3D point clouds, annotated with fine-grained domain semantics.<n>We then evaluate 18 state-of-the-art MLLMs across three manufacturing tasks, namely verification, structural surface inspection, and assembly verification, revealing significant performance gaps.
  arXiv  Detail & Related papers  (2026-04-08T12:23:27Z)
• AgriDoctor: A Multimodal Intelligent Assistant for Agriculture [45.77373971125537]
  AgriDoctor is a modular and multimodal framework designed for intelligent crop disease diagnosis and agricultural knowledge interaction.<n>To facilitate effective training and evaluation, we construct AgriMM, a benchmark comprising 400000 annotated disease images, 831 expert-curated knowledge entries, and 300000 bilingual prompts for intent-driven tool selection.<n>Experiments demonstrate that AgriDoctor, trained on AgriMM, significantly outperforms state-of-the-art LVLMs on fine-grained agricultural tasks.
  arXiv  Detail & Related papers  (2025-09-21T11:51:57Z)
• AgriGPT: a Large Language Model Ecosystem for Agriculture [16.497060004913806]
  AgriGPT is a domain-specialized Large Language Models ecosystem for agriculture usage.<n>At its core, we design a scalable data engine that compiles credible data sources into Agri-342K, a high-quality, standardized question-answer dataset.<n>We employ Tri-RAG, a three-channel Retrieval-Augmented Generation framework combining dense retrieval, sparse retrieval, and multi-hop knowledge graph reasoning.
  arXiv  Detail & Related papers  (2025-08-12T04:51:08Z)
• AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock [77.95897723270453]
  Crops, fisheries and livestock form the backbone of global food production, essential to feed the ever-growing global population.<n> Addressing these issues requires efficient, accurate, and scalable technological solutions, highlighting the importance of artificial intelligence (AI)<n>This survey presents a systematic and thorough review of more than 200 research works covering conventional machine learning approaches, advanced deep learning techniques, and recent vision-language foundation models.
  arXiv  Detail & Related papers  (2025-07-29T17:59:48Z)
• AgriEval: A Comprehensive Chinese Agricultural Benchmark for Large Language Models [19.265932725554833]
  We propose AgriEval, the first comprehensive Chinese agricultural benchmark with three main characteristics.<n>AgriEval covers six major agriculture categories and 29 subcategories within agriculture, addressing four core cognitive scenarios.<n>AgriEval comprises 14,697 multiple-choice questions and 2,167 open-ended question-and-answer questions, establishing it as the most extensive agricultural benchmark available to date.
  arXiv  Detail & Related papers  (2025-07-29T12:58:27Z)
• A Multimodal Benchmark Dataset and Model for Crop Disease Diagnosis [5.006697347461899]
  We present the crop disease domain multimodal dataset, a pioneering resource designed to advance the field of agricultural research.<n>The dataset comprises 137,000 images of various crop diseases, accompanied by 1 million question-answer pairs that span a broad spectrum of agricultural knowledge.<n>We demonstrate the utility of the dataset by finetuning state-of-the-art multimodal models, showcasing significant improvements in crop disease diagnosis.
  arXiv  Detail & Related papers  (2025-03-10T06:37:42Z)
• Agri-LLaVA: Knowledge-Infused Large Multimodal Assistant on Agricultural Pests and Diseases [49.782064512495495]
  We construct the first multimodal instruction-following dataset in the agricultural domain.<n>This dataset covers over 221 types of pests and diseases with approximately 400,000 data entries.<n>We propose a knowledge-infused training method to develop Agri-LLaVA, an agricultural multimodal conversation system.
  arXiv  Detail & Related papers  (2024-12-03T04:34:23Z)
• AgroGPT: Efficient Agricultural Vision-Language Model with Expert Tuning [30.034193330398292]
  We propose an approach to construct instruction-tuning data that harnesses vision-only data for the agriculture domain.<n>We utilize diverse agricultural datasets spanning multiple domains, curate class-specific information, and employ large language models (LLMs) to construct an expert-tuning set.<n>We expert-tuned and created AgroGPT, an efficient LMM that can hold complex agriculture-related conversations and provide useful insights.
  arXiv  Detail & Related papers  (2024-10-10T22:38:26Z)
• Generating Diverse Agricultural Data for Vision-Based Farming Applications [74.79409721178489]
  This model is capable of simulating distinct growth stages of plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. Our dataset includes 12,000 images with semantic labels, offering a comprehensive resource for computer vision tasks in precision agriculture.
  arXiv  Detail & Related papers  (2024-03-27T08:42:47Z)
• Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis [110.30849704592592]
  We present Agriculture-Vision: a large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns. Each image consists of RGB and Near-infrared (NIR) channels with resolution as high as 10 cm per pixel. We annotate nine types of field anomaly patterns that are most important to farmers.
  arXiv  Detail & Related papers  (2020-01-05T20:19:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.