Brief description
This dataset is a collection of 5,049 images of cotton leaf surfaces acquired with a hand-held microscope to develop deep learning models for leaf hairiness and assist Cotton breeders in their variety selection efforts. These images were collected from two populations (A: 3,276 images; B: 1,773 images) over the 2021-2022 season in a field located at Australian Cotton Research Institute, -30.21, 149.60, Narrabri, NSW, Australia. Populations and genotypes have been anonymized to protect germplasm Intellectual Property.This dataset is being released together with our HairNet2 paper (Farazi et al 2024). See below for links to related Datasets and Publications.
Lineage
Plant genotypes and growth conditions:Two cotton populations called A and B, were selected for their heterogeneous leaf hairiness, with population A being generally less hairy than population B. Both populations were planted in the summer growing season of 2021-22 at ACRI. Seeds of each genotype were planted in a field on the 23rd of October 2021 at a planting density of 10-12 plants/m2 in rows spaced at 1m. Each genotype was grown in a single 13m plot.
Leaf selection and imaging
Leaf samples from these plant populations were collected on the 2nd and 6th of March 2022 (at 19 weeks, first open boll stage). Leaf 3 was harvested from 10 plants per genotype, placed in a paper bag and imaged the same day using the same protocol and equipment as in Rolland, Farazi et al 2022, with the following distinctions:
- for population A, two images were collected per leaf: one along the central midvein and one on the leaf blade.
- for population B, one image was collected per leaf: along the central midvein.
The abaxial side of leaves were imaged at a magnification of about 31x with a portable AM73915 Dino-lite Edge 3.0 (AnMo Electronics Corporation, Taiwan) microscope equipped with a RK-04F folding manual stage (AnMo Electronics Corporation, Taiwan) and connected to a digital tablet running DinoCapture 2.0 (AnMo Electronics Corporation, Taiwan). 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.
Visual scoring of images by human expert
A human expert scored all CotLeaf-X images using arbitrary ordinal scales (0 − 5 for population A and 2 − 5.5 for population B), where higher numbers corresponded to images with more trichomes.
Data time period: 2021-01-01 to 2022-01-01
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 Improvement (Incl. Selection and Breeding) |
Crop and Pasture Production |
Crop and Pasture Protection (Incl. Pests, Diseases and Weeds) |
Deep Learning |
Gin trash |
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 |
Spidermite |
Trichome |
Whitefly |
Yield |
User Contributed Tags
Login to tag this record with meaningful keywords to make it easier to discover
Identifiers
- DOI : 10.25919/EQHX-1X73
- Local : 102.100.100/608275
