arXiv Machine Learning August 20, 2026 By Andrea Morales-Garz\'on, Salvador L\'opez-Joya, Miguel L\'opez-P\'erez, Maria J. Martin-Bautista

You Are What You Prompt: Prompt Quality, Domain Shift, and Uncertainty in Agrifood Vision-Language Models

Read the original on arXiv Machine Learning →

The paper studies how prompt quality affects vision‑language models in the agrifood domain. It evaluates Zero‑Shot Prompt Ensembling (ZPE) on CLIP and SigLIP across four datasets, showing that ZPE offers limited gains on in‑distribution data but significantly improves accuracy and calibration when the domain shifts. The authors also introduce Prompt‑based Inconsistency Detection (PID), which uses prompt disagreement to detect failures under severe domain shift, outperforming standard confidence measures.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 30

PlantExpertVQA: A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Science

arXiv:2508. 17117v3 Announce Type: replace-cross Abstract: Existing plant-disease datasets target classification and detection, leaving vision-language models unable to support interactive, reasoning-based diagnosis.

By Syed Nazmus Sakib, Nafiul Haque, Mohammad Zabed Hossain, Shifat E. Arman

llms nlp fine-tuning multimodal benchmarks

arXiv Computer Vision

Sep 23

BananaVLM: A Domain-Adapted Vision Language Model for Banana Crop Disease Diagnosis

arXiv:2609.25040v1 Announce Type: cross Abstract: Banana crop diseases threaten food security across the world, yet field diagnosis remains difficult because of limited expert access and visual simil...

By Sangam Kumar Jena, Pandarasamy Arjunan

llms fine-tuning multimodal benchmarks

Hugging Face Trending Papers

Aug 9

TomaMMU: A Comprehensive Multimodal Understanding Benchmark for Tomato Leaf Diseases

To address this gap, we introduce TomaMMU, a large-scale Tomato leaf disease MultiModal Understanding dataset, alongside TomaBench, a benchmark for evaluating VLMs on tomato disease understanding. TomaMMU comprises 28,808 high-quality images spanning 15 categories and 213,119 human-annotated visual question-answer pairs, generated through a three-stage pipeline comprising Data Collection, Human Annotation, and Question-Answer Generation.

fine-tuning multimodal benchmarks

arXiv Computer Vision

Sep 10

Vision-language models know more about agriculture than they show and rubric-grounded verifications close the gap

arXiv:2609.09417v1 Announce Type: new Abstract: Vision-language models (VLMs) show promise for agricultural classification, but zero-shot performance on disease, pest, damage, quality, and species id...

By Earl Ranario, Jared Smith, Lars Lundqvist, Urmil Jatin Chandarana

llms multimodal benchmarks

arXiv AI

Jul 22

Attributes Should Come from Images, Not Class Names: Distribution-Conditioned Attribute Selection for Vision-Language Models

arXiv:2607. 18695v1 Announce Type: cross Abstract: A popular route to interpretable zero-shot classification asks a large language model (LLM) to describe each class name and prompts CLIP with the resulting descriptors.

By Gautam Rajendrakumar Gare, Jia Shi, Zhiqiu Lin, Deepak Pathak, John Galeotti, Deva Ramanan

llms rag multimodal

arXiv AI

Aug 11

TomaMMU: A Comprehensive Multimodal Understanding Benchmark for Tomato Leaf Diseases

arXiv:2608. 08727v1 Announce Type: cross Abstract: To address this gap, we introduce TomaMMU, a large-scale Tomato leaf disease MultiModal Understanding dataset, alongside TomaBench, a benchmark for evaluating VLMs on tomato disease understanding.

By Gia-Han Truong, Khang Nguyen Quoc, Luyl-Da Quach

fine-tuning multimodal benchmarks

One email a morning, machine-written

Related stories

PlantExpertVQA: A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Science

BananaVLM: A Domain-Adapted Vision Language Model for Banana Crop Disease Diagnosis

TomaMMU: A Comprehensive Multimodal Understanding Benchmark for Tomato Leaf Diseases

Vision-language models know more about agriculture than they show and rubric-grounded verifications close the gap

Attributes Should Come from Images, Not Class Names: Distribution-Conditioned Attribute Selection for Vision-Language Models

TomaMMU: A Comprehensive Multimodal Understanding Benchmark for Tomato Leaf Diseases