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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…
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…
AgRowStitch: A High-fidelity Image Stitching Pipeline for Ground-based Agricultural Images
Isaac Kazuo Uyehara, Heesup Yun, Earl Ranario +1
Agricultural imaging often requires individual images to be stitched together into a final mosaic for analysis. However, agricultural images can be particularly challenging to stit…