5 papers · 1 filter
FloraForge: LLM-Assisted Procedural Generation of Editable and Analysis-Ready 3D Plant Geometric Models For Agricultural Applications
Mozhgan Hadadi, Talukder Z. Jubery, Patrick S. Schnable +4
Accurate 3D plant models are crucial for computational phenotyping and physics-based simulation; however, current approaches face significant limitations. Learning-based reconstruc…
MaizeField3D: A Curated 3D Point Cloud and Procedural Model Dataset of Field-Grown Maize from a Diversity Panel
Elvis Kimara, Mozhgan Hadadi, Jackson Godbersen +6
The development of artificial intelligence (AI) and machine learning (ML) based tools for 3D phenotyping, especially for maize, has been limited due to the lack of large and divers…
Accessing the Effect of Phyllotaxy and Planting Density on Light Use Efficiency in Field-Grown Maize using 3D Reconstructions
Nasla Saleem, Talukder Zaki Jubery, Aditya Balu +5
High-density planting is a widely adopted strategy to enhance maize productivity, yet it introduces challenges such as increased interplant competition and shading, which can limit…
MaizeEar-SAM: Zero-Shot Maize Ear Phenotyping
Hossein Zaremehrjerdi, Lisa Coffey, Talukder Jubery +6
Quantifying the variation in yield component traits of maize (Zea mays L.), which together determine the overall productivity of this globally important crop, plays a critical role…
Procedural Generation of 3D Maize Plant Architecture from LIDAR Data
Mozhgan Hadadi, Mehdi Saraeian, Jackson Godbersen +8
This study introduces a robust framework for generating procedural 3D models of maize (Zea mays) plants from LiDAR point cloud data, offering a scalable alternative to traditional…