#Current projects (GWFSS)
Global wheat full semantic segmentation
Time Line
Aim to train a semantic segmentation of wheat organs (stems, leaves, flowers) … under field conditions to enable precision breeding…
Until 2023.10
Tagging of 40,000 images
Until 2024.05
Model-assisted labeling of organs, soil, residues, and weeds in >4000 images
2024.05 to 2024.07
Image-analysis data challenge
After 2024.10
Publication of the result at Computer Vision Problems in Plant Phenotyping (CVPPP) 2024
Deep learning methods for image processing are rapidly advancing and imaging techniques have become a standard for classification and quantification in agriculture. However, agricultural datasets assembled by domain experts are still comparably small. The global wheat consortium brings together this domain knowledge to assemble a large training set. We aim is to define a balanced dataset for training and validation containing the most relevant features observable for field-grown wheat. Image information will be enhanced by metadata, such as the developmental stage, genotype or agricultural treatment.
Contributed images fulfil the following conditions:
