AgriScope: Pixel-Grounded Multimodal Understanding for Agricultural Images

arXiv:2609.20325 · cs.CV · Submitted 2026-09-17 · Read on arXiv

cs.CV

Submitted: 2026-09-17

Updated: 2026-09-17

Code: https://github.com/boudiafA/AgriScope

Terminology

• Grounding — 90× in this paper · explained in 3 episodes
• Visual Grounding — 30× in this paper · explained in 2 episodes
• Benchmark — 18× in this paper · explained in 2 episodes
• Segmentation Masks — 18× in this paper · explained in 2 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• Grounded Caption Generation — 11× in this paper
• Instruction Tuning — 10× in this paper · explained in 2 episodes
• Referring Expression Segmentation — 8× in this paper
• Embeddings — 7× in this paper · explained in 2 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Verification — 6× in this paper · explained in 2 episodes
• Visual Question Answering — 6× in this paper
• Generalization — 5× in this paper · explained in 13 episodes
• Multimodal Learning — 5× in this paper · explained in 2 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Alignment — 4× in this paper · explained in 1 episode
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Human-in-the-Loop — 4× in this paper · explained in 3 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• F1 Score — 3× in this paper · explained in 3 episodes
• International Conference on Learning Representations — 3× in this paper
• Mask Generation — 3× in this paper · explained in 2 episodes
• Multimodal Reasoning — 3× in this paper · explained in 5 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Unified Framework — 3× in this paper · explained in 5 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Fidelity — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Large Language Model (LLM) — 2× in this paper · explained in 3 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• CIDEr and Agricultural Semantic Fidelity — 1× in this paper
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Low-Rank Adaptation (LoRA) — 1× in this paper · explained in 9 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Self-Supervised Learning (SSL) — 1× in this paper · explained in 4 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• A Multimodal Benchmark Dataset and Model for Crop Disease Diagnosis
• Agri-LLaVA: Knowledge-Infused Large Multimodal Assistant on Agricultural Pests and Diseases
• AgriDoctor: A Multimodal Intelligent Assistant for Agriculture
• AgriGPT-VL: Agricultural Vision-Language Understanding Suite
• AgroBench: Vision-Language Model Benchmark in Agriculture
• Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind
• BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learning
• SAM 3: Segment Anything with Concepts
• AgriChat: A Multimodal Large Language Model for Agriculture Image Understanding
• DINOv3
• Qwen2.5 Technical Report
• CapRL: Stimulating Dense Image Caption Capabilities via Reinforcement Learning
• LLaVA-OneVision: Easy Visual Task Transfer
• Qwen2.5-VL Technical Report
• InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
• Qwen3-VL Technical Report
• Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos

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