Blog icon

HairNet for improved cotton breeding

HairNet is a new artificial intelligence and machine learning model that can score leaf hairiness in cotton. This assists breeders to identify cotton plants with beneficial traits for improved commercial production. The HairNet model offers a number of benefits compared to the manual scoring method cotton breeders currently rely on.

[CSIRO logo appears on screen.]

[Timelapse shot of a busy city intersection with traffic] Voiceover: Busy roads are filled with visual and auditory cues that we need to perceive, analyse and understand to make safe decisions.

[Computer graphic of a computer processing data] Artificial intelligence and computer vision have been harnessed to engineer self-driving cars.

[Computer graphic of cars sensing each other as they travel down a road] These can autonomously make sense of their surroundings by detecting and identifying objects to guide decisions.

[Drone shot of a large cotton field] Breeding better crop varieties also requires many observations to be made to select the top of the crop.

[Shot of a cotton breeder inspecting cotton in a field] And a lot of these are still done manually, by a number of different people, which presents a number of limitations.

[Scene of a CSIRO researcher looking down a microscope, followed by a data scientist coding at a computer] At CSIRO, we have combined our expertise in crop breeding, microimaging, and computer vision to build HairNet, a machine learning-powered solution to score leaf hairiness.

[Shot of a glasshouse with cotton, followed by a person in a cotton field] In Cotton, leaf hairiness is a key indicator of fibre yield, its economical value and the ability of the plant to resist certain insects.

[Shot of a person looking at the hairs on a cotton leaf, and two people in a glasshouse looking at cotton leaves] Each year, breeders visually inspect and score leaf hairiness to select the best lines. But different people may give the same plant a slightly different score.

[Shot of cotton plants in a glasshouse] And this test relies on the reflection of sunlight on the surface of the leaf so it can only be done on cloudless days.

[Scene of a cotton leaf being put under an imaging system, with the lead appearing magnified on a computer screen] We are using a simple imaging system to generate images of leaves. These images are then fed to our HairNet model which can predict leaf hairiness with 95% accuracy, without operator bias and regardless of the weather!

[Sunset scene of three cotton harvesters working in a field] This new tool will give cotton breeders robust measurements on any day, which is important in the middle of a busy breeding season.

[Shot of cotton shirts blowing in the wind on a clothesline, followed by a drone shot of a cotton field] Beyond Cotton, a similar approach could be used to score a range of other important characteristics such as how well plants can utilise water, or resist disease.

[Shot of a farmer standing in the field at sunset] With tools like HairNet we are building a future where artificial intelligence assists humans make better decisions to breed the future crops we need.


Share & embed this video



Embed code

<iframe src="//" width="640" height="360" frameborder="0" allow="autoplay; fullscreen" allowfullscreen></iframe>


Agricultural blog articles

Contact us

Find out how we can help you and your business. Get in touch using the form below and our experts will get in contact soon!

CSIRO will handle your personal information in accordance with the Privacy Act 1988 (Cth) and our Privacy Policy.

First name must be filled in

Surname must be filled in

I am representing *

Please choose an option

Please provide a subject for the enquriy

0 / 100

We'll need to know what you want to contact us about so we can give you an answer

0 / 1900

You shouldn't be able to see this field. Please try again and leave the field blank.