Brief description
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.
Lineage
Genotype Selection:A total of 27 Gossypium hirsutum Cotton genotypes were selected based on their known leaf hairiness. Genotypes were anonymised to protect germplasm intellectual property. Various combinations of these genotypes were grown at two different Australian sites (Narrabri, New South Wales & Canberra, Australian Capital Territory), in the field or controlled glasshouse environment, and over two years (2019-2020 and 2020-2021). For details refer to Rolland et al 2021 (link attached to this submission).
Field experiments - Narrabri
Seed of selected genotype were planted on Oct. 21 2019 and Nov. 6 2020, at planting density of 10 - 12 plants m-2 in rows spaced at 1 m. Each genotype was grown in a single 13 m row. The soil of the site is a uniform grey cracking clay. Nitrogen was applied as anhydrous ammonia approximately 12 weeks before planting at a rate of 200 kg N ha-1. Plants were furrow irrigated every 10 to 14 d (approximately 1 ML ha-1 applied at each irrigation) from December through to March, according to crop requirements. Each experiment was managed according to its individual requirements for irrigation and pest control, with all plots receiving the same management regime.
Glasshouse experiments - Narrabri
Plants were grown in temperature-controlled glasshouses. About 15 seeds of each genotype were sown in 8 L plastic pots filled with soil on Sept. 6 2019 and Nov. 2 2020, respectively. The soil was obtained from cotton fields as above. To improve the nutrient status of the potting mix 10 g of MULTIgro® basal fertiliser was dissolved into the soil before planting. A 10 mm layer of sand was added to the surface of the pots to reduce surface evaporation and assist in seedling emergence. Once emerged seedlings had reached the three-leaf stage, pots were thinned down to two plants per pot. Plants were grown at 18 °C night and 32 °C during the day, under natural light conditions.
Glasshouse experiment - Canberra
Plants were grown in temperature-controlled glasshouses. Eight seeds of selected genotypes were sown in 5 L plastic pots filled with potting mix on Nov. 30 2020. The pots were filled with a 60:40 compost:perlite soil mix. Osmocote® Exact Standard 3-4M was sprinkled on the top layer of soil before flowering. Two weeks after sowing, pots were thinned down to two plants per pot. Plants were grown at 18 °C night and 28 °C during the day, under natural light conditions.
Leaf selection and harvesting:
Leaves were numbered in ascending number from the tip of the main stem, with the first fully opened leaf called leaf one. Leaves 3 and 4 from ten individual plants were harvested by cutting their petiole in a proximal position. Harvested leaves were placed in paper bags and imaged within the same day. In the 2019-2020 glasshouse experiment, a few plants died or had a missing leaf, in which case there may be genotypes for which leaves 3 and 4 were harvested from less than 10 plants.
Leaf imaging:
Single leaves were imaged at a magnification of about 31x with a portable AM73915 Dino-lite Edge 3.0 microscope equipped with a RK-04F folding manual stage and connected to a digital tablet running DinoCapture 2.0. Images were captured on the abaxial side of the leaf, along the 3 central mid-veins. An average of 3 to 5 images were captured in a proximal to distal fashion along each one of the 3 mid-veins, yielding a total of about 9 to 15 images per leaf. The exact angle of the mid-vein in each image was not fixed. However, either end of the mid-vein was always cut by the left and right borders of the field of view, and never by the top and bottom ones.
Data time period: 2019-01-01 to 2021-01-01
Subjects
Agricultural, Veterinary and Food Sciences |
Agronomy |
Artificial Intelligence |
Artificial Intelligence Not Elsewhere Classified |
Biological Sciences |
Computer Vision |
Computer Vision and Multimedia Computation |
Cotton |
Crop and Pasture Production |
Crop and Pasture Production Not Elsewhere Classified |
Crop and Pasture Protection (Incl. Pests, Diseases and Weeds) |
Deep Learning |
Hair |
Hairiness |
Information and Computing Sciences |
Image Processing |
Leaf |
Machine learning |
Plant |
Plant Biology |
Plant Biology Not Elsewhere Classified |
Plant Developmental and Reproductive Biology |
Plant Pathology |
Plant Physiology |
Pubescence |
Trichome |
gin trash |
whitefly |
yield |
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Identifiers
- DOI : 10.25919/9VQW-7453
- Local : 102.100.100/432207
