1 citations · 2 across the 4 of their papers we have counts for
5 papers
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…
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…
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…
LAESI: Leaf Area Estimation with Synthetic Imagery
Jacek Kałużny, Yannik Schreckenberg, Karol Cyganik +7
We introduce LAESI, a Synthetic Leaf Dataset of 100,000 synthetic leaf images on millimeter paper, each with semantic masks and surface area labels. This dataset provides a resourc…
Generating Diverse Agricultural Data for Vision-Based Farming Applications
Mikolaj Cieslak, Umabharathi Govindarajan, Alejandro Garcia +7
We present a specialized procedural model for generating synthetic agricultural scenes, focusing on soybean crops, along with various weeds. This model is capable of simulating dis…