5 papers
GrowFields: Compositional 4D Neural Fields for Topology-Changing Plant Growth
Joaquin Gajardo, Michele Volpi, Marko Mihajlovic +3
Quantifying plant growth dynamics from sparse longitudinal 3D observations is fundamental for agriculture and plant sciences. Yet, plants pose unique challenges: they undergo intri…
3D Reconstruction and Knowledge Distillation to Improve Multi-View Image Models to Explore Spike Volume Estimation in Wheat
Olivia Zumsteg, Jannis Widmer, Yann Bourdé +4
Accurate estimation of wheat spike volume is important for yield component analysis and stress resilience assessment, yet field-based measurement remains challenging. Active 3D sen…
Fine-Tuned Vision Transformers Capture Complex Wheat Spike Morphology for Volume Estimation from RGB Images
Olivia Zumsteg, Nico Graf, Aaron Haeusler +4
Estimating three-dimensional morphological traits such as volume from two-dimensional RGB images presents inherent challenges due to the loss of depth information, projection disto…
FoMo4Wheat: Toward reliable crop vision foundation models with globally curated data
Bing Han, Chen Zhu, Dong Han +22
Vision-driven field monitoring is central to digital agriculture, yet models built on general-domain pretrained backbones often fail to generalize across tasks, owing to the intera…
Wheat3DGS: In-field 3D Reconstruction, Instance Segmentation and Phenotyping of Wheat Heads with Gaussian Splatting
Daiwei Zhang, Joaquin Gajardo, Tomislav Medic +5
Automated extraction of plant morphological traits is crucial for supporting crop breeding and agricultural management through high-throughput field phenotyping (HTFP). Solutions b…