40 citations · 41 across the 9 of their papers we have counts for
9 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…
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…
Enabling Plant Phenotyping in Weedy Environments using Multi-Modal Imagery via Synthetic and Generated Training Data
Earl Ranario, Ismael Mayanja, Heesup Yun +2
Accurate plant segmentation in thermal imagery remains a significant challenge for high throughput field phenotyping, particularly in outdoor environments where low contrast betwee…
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…
CMAViT: Integrating Climate, Managment, and Remote Sensing Data for Crop Yield Estimation with Multimodel Vision Transformers
Hamid Kamangir, Brent. S. Sams, Nick Dokoozlian +2
Crop yield prediction is essential for agricultural planning but remains challenging due to the complex interactions between weather, climate, and management practices. To address…
VisTA-SR: Improving the Accuracy and Resolution of Low-Cost Thermal Imaging Cameras for Agriculture
Heesup Yun, Sassoum Lo, Christine H. Diepenbrock +2
Thermal cameras are an important tool for agricultural research because they allow for non-invasive measurement of plant temperature, which relates to important photochemical, hydr…
End-to-end deep learning for directly estimating grape yield from ground-based imagery
Alexander G. Olenskyj, Brent S. Sams, Zhenghao Fei +4
Yield estimation is a powerful tool in vineyard management, as it allows growers to fine-tune practices to optimize yield and quality. However, yield estimation is currently perfor…