CotLeaf-1: Cotton Leaf Surface Images dataset 2019-21

This dataset is a collection of 13,597 images of cotton leaf surfaces acquired with a hand-held microscope to develop deep learning models to classify images based on leaf hairiness and assist Cotton breeders in their variety selection efforts.

These images were collected from 27 genotypes grown across 2 seasons (2019-2020 and 2020-2021), 2 sites (Australian Cotton Research Institute, -30.21, 149.60, Narrabri, NSW, Australia and CSIRO Black Mountain Laboratories, -35.27, 149.11, Canberra, Australian Capital Territory, Australia) and two growth conditions (Field and Glasshouse). Genotypes have been anonymized to protect germplasm Intellectual Property.

Note 1: This dataset was released with our HairNet paper (Rolland et al 2022, see link below). At the time of publishing Rolland, V., Farazi, M.R., Conaty, W.C. et al. HairNet: a deep learning model to score leaf hairiness, a key phenotype for cotton fibre yield, value and insect resistance. Plant Methods 18, 8 (2022). https://doi.org/10.1186/s13007-021-00820-8, this dataset was called 'Cotton leaf surface image dataset to build deep learning models for leaf hairiness trait (2019-2021)'. It has since being renamed 'CotLeaf-1: Cotton Leaf Surface Images dataset 2019-21'.

Note 2: if you intend to use this dataset in conjunction with CotLeaf-2, CotLeaf-X or AnnCoT datasets, then use the CotLeaf-1 Json file attached to the CotLeaf-2 collection (see link below).

See below for related Datasets and Publications.