1 citations · 1 across the 1 of their papers we have counts for
4 papers
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 synthetic benchmark to evaluate the performance of vision language models (VLMs) in generating plant simulation configurations for digital twins. While func…
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…