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.

The GWFSS_v1.0 dataset comprises 1096 ground truth-truth labelled images and 52,078 images without labels described in Wang et al. 2025 The download links can be found in the data availability statement at the end of the paper.