1 citations · 1 across the 5 of their papers we have counts for
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Vision-language models know more about agriculture than they show and rubric-grounded verifications close the gap
Earl Ranario, Jared Smith, Lars Lundqvist +2
Vision-language models (VLMs) show promise for agricultural classification, but zero-shot performance on disease, pest, damage, quality, and species identification remains poor, an…
Can Synthetic Data Overcome the Generalization Limits of AI-Based Flower and Pod Detection Across Cowpea Breeding Genotypes and Environments?
Hamid Kamangir, Jonathan Berlingeri, Earl Ranario +6
High-throughput phenotyping requires AI-enabled computer vision models that generalize across genotypes, locations, and growing seasons, yet such models often lose accuracy under n…
Does Your VFM Speak Plant? The Botanical Grammar of Vision Foundation Models for Object Detection
Lars Lundqvist, Earl Ranario, Hamid Kamangir +4
Vision foundation models (VFMs) offer the promise of zero-shot object detection without task-specific training data, yet their performance in complex agricultural scenes remains hi…
Using Vision Language Foundation Models to Generate Plant Simulation Configurations via In-Context Learning
Heesup Yun, Isaac Kazuo Uyehara, Earl Ranario +4
This paper introduces a benchmark for evaluating whether vision-language models (VLMs) can generate plant simulation configurations from imagery using in-context learning. We study…
AGILE: A Diffusion-Based Attention-Guided Image and Label Translation for Efficient Cross-Domain Plant Trait Identification
Earl Ranario, Lars Lundqvist, Heesup Yun +2
Semantically consistent cross-domain image translation facilitates the generation of training data by transferring labels across different domains, making it particularly useful fo…