In a world first, UNSW and Sydney Botanic Garden scientists will use data from millions of botanical specimens held in botanic gardens around the world to study and combat the impacts of climate change on flora. trained an AI to unleash
“The specimen collection is a wonderful time capsule of herbarium,” says associate professor Will Cornwell, lead author of the study. “The National Herbarium of New South Wales alone adds more than 8,000 specimens each year, so it is no longer possible to examine them all by hand.”
Using a new machine-learning algorithm to process more than 3,000 leaf samples, the research team found that within a single species, leaf size increased in temperate climates, in contrast to frequently observed interspecific patterns. I found that it doesn’t increase.
Published in American Journal of Botanythis study not only revealed that non-climatic factors strongly influence leaf size within plant species, but also used AI to transform static specimen collections and explore the effects of climate change. demonstrated how to quickly and effectively document
The Herbarium Collection Moves to the Digital World
Herbaria is a scientific library of botanical specimens that has existed since at least the 16th century.
“Historically, a valuable scientific undertaking was to go out and collect plants and store them in herbaria. / says the professor. Mr. Cornwell is a Researcher at the School of BEES and a member of the UNSW Data Science His Hub.
Read more: The world’s first flower was pollinated by insects
A few years ago there was a move to transfer these collections online to facilitate scientific collaboration.
“Specimen collections used to be confined to small boxes in specific locations, but the world is very digital now. For this purpose, efforts were made to scan specimens and create high-resolution digital copies.”
The largest herbarium imaging project was carried out at the Sydney Botanic Gardens, transforming over one million herbarium specimens at the National Botanic Gardens of New South Wales into high-resolution digital images.
“The digitization project took over two years, but soon after it was completed, one of the researchers, Dr. Jason Bragg, contacted me from the Sydney Botanic Gardens. I wanted to know how to incorporate machine learning into some of the digital images.”
Dr Bragg said: Cornwell is working on developing a model to detect leaves in plant images and use those large datasets to study the relationship between leaf size and climate. ”
Measure leaf size with “computer vision”
With Dr. Bragg of Sydney Botanic Gardens and UNSW Honors Professor Brendan Wilde. Cornwell has created an algorithm that can be automated to detect and measure leaf size in scanned herbarium samples of two plant genera. Syzygium (commonly known as liripili, blush cherry, or satina) and fig (a genus of about 850 species of trees, shrubs and vines).
Machine learning algorithms developed by the research team measure and identify plant specimens. Photo: accessories.
“This type of AI is called convolutional neural networks, also called computer vision,” says Professor A/. Cornwell. This process essentially teaches AI how to see and identify plant components in the same way humans do.
“We had to build a training data set to teach the computer that this is a leaf, this is a stem, this is a flower,” says Professor A/. Cornwell. “So we basically taught the computer to locate the leaves and measure their size.
“Measuring leaf size is nothing new, as many people do it. But the speed with which we can process these specimens and record individual features is a new development.”
Read more: Thousands of native plants go unphotographed, but citizen scientists can help fill the gap
Breaking out of frequently observed patterns
A general rule of thumb in the botanical world is that plants grow larger leaves in humid climates, such as rainforests, than in dry climates, such as deserts.
“And it’s a very consistent pattern found in interspecies leaves around the world,” says Professor A/. Cornwell. “The first test we did was to see if we could reconstruct that relationship from machine-learned data. Since there is a lot of data in the
Machine learning algorithms were developed, validated and applied to analyze leaf size–climate relationships within and between species. Syzygium and fig plant.
The results of this test were astonishing. The research team found that although this pattern is seen across different plant species, the same correlation is not seen within a single species worldwide. This is probably due to a different process known as gene flow. Active within species. This process may weaken plant adaptations on a local scale and prevent leaf size–climate relationships from developing within species.
Using AI to predict future climate change responses
The machine learning approach used here for leaf detection and measurement, although not pixel-perfect, provided a good level of accuracy for examining associations between leaf characteristics and climate.
“But the world is changing so fast and there is so much data that this kind of machine learning technique can be used to effectively document the impacts of climate change,” said A/ says the professor.cornwell
Additionally, machine learning algorithms can be trained to identify trends that are not readily apparent to human researchers. This could lead to new insights into plant evolution and adaptation, as well as predictions about how plants will respond to future climate change impacts.
