40 citations · 41 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
A Vision Language Model for Generating Procedural Plant Architecture Representations from Simulated Images
Heesup Yun, Isaac Kazuo Uyehara, Ioannis Droutsas +4
Three-dimensional (3D) procedural plant architecture models have emerged as an important tool for simulation-based studies of plant structure and function, extracting plant archite…
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…