3 papers
cs.CV2026
Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data
Samy Mounir, Mikolaj Cieslak, Najmeddine Dhieb +8
Vision-based automation is an excellent candidate for reducing manual labor in greenhouse crop production and phenotyping. However, progress is constrained by the lack of annotated…
cs.CV2026
The Effects of Synthetic Data and Label Distribution on Canola Branch Counting
Amirsalar Darvishpour, Mikolaj Cieslak, Adam Runions
Collecting annotated plant images for automated phenotyping is often slow and expensive. Plant models simulating growth and development can generate unlimited synthetic images with…
cs.CV2024
Importance of realism in procedurally-generated synthetic images for deep learning: case studies in maize and canola
Nazifa Azam Khan, Mikolaj Cieslak, Ian McQuillan
Artificial neural networks are often used to identify features of crop plants. However, training their models requires many annotated images, which can be expensive and time-consum…