Insect wingbeats will help quantify biodiversity — ScienceDaily

Insects are vital as plant pollinators, as a food source for a wide variety of animals and as decomposers of dead material in nature. But in recent decades, they have been struggling. It is estimated that 40 percent of insect species are in decline and a third of them are endangered.

Therefore, it is more important than ever to monitor insect biodiversity, so as to understand their decline and hopefully help them out. So far, this task has been difficult and resource-intensive. In part, this is due to the fact that insects are small and very dynamic. Furthermore, scientific researchers and public agencies need to set up traps, capture insects and study them under the microscope.

To overcome these hurdles, University of Copenhagen researchers have developed a method that uses the data obtained from an infrared sensor to recognize and detect the wingbeats of individual insects. The AI method is based on unsupervised machine learning — where the algorithms can group insects belonging to the same species without any human input. The results from this method could provide information about the diversity of insect species in a natural space without anyone needing to catch and count the critters by hand.

“Our method makes it much easier to keep track of how insect populations are evolving. There has been a huge loss of insect biomass in recent years. But until we know exactly why insects are in decline, it is difficult to develop the right solutions. This is where our method can contribute new and important knowledge,” states PhD student Klas Rydhmer of the Department of Geosciences and Natural Resource Management at UCPH’s Faculty of Science, who helped develop the method.

Advanced artificial intelligence

The researchers have already developed an algorithm that identifies pests in agricultural fields. But instead of identifying insects as pests, the researchers have been able to develop this new algorithm to identify and count various insect populations in nature based on the measurements obtained from the sensor.

“The sensor is a bit like the wildlife surveillance cameras used to monitor the movements of larger animals in nature. But instead of snapping a photo, the sensor measures insects that have has flown into the light source. The algorithm then uses the insect’s wingbeat to identify them into different groups,” explains Assistant Professor Raghavendra Selvan of the Department of Computer Science, who led the development of the artificial intelligence used in the sensor.

The algorithm distinguishes insects by their silhouettes when their wings are folded out, as it is only then that their physical differences become most apparent. It then compares the silhouettes of different insect recordings, and puts similar silhouettes into the same group which can then be used to determine the insect that most likely flew through the light beam.

Prototype to be released in spring

When insects emerge in full force come spring, scientists will be using the initial prototype to venture out into nature and collect real-world data.

Until now, researchers have tested the algorithm and artificial intelligence using a large image database of insects recordings obtained in controlled conditions and some real-world data, where results have been promising.

“We will test the sensor in different landscapes, including heathland, forests and agricultural areas, to see how it works out in the real world. But also, to feed the algorithm more data, so that it can become even more accurate,” says Raghavendra Selvan.

According to the researchers, their invention makes it possible to monitor many geographical areas more thoroughly than has been possible in the past. At the same time, the invention makes it less resource-intensive to keep a close eye on insects, which make up 80 percent of all terrestrial animal species.

“Today, it is impossible to afford the kind of monitoring needed to gain a more precise overview of how our insects are doing. This sensor only needs humans to place it out in the wild. Once there, it begins collecting data on local insect populations,” concludes Klas Rydhmer.

Background:

• Insects are the largest, most diverse group of described animal species on Earth. They make up about 80% of all terrestrial animal species on the planet.
• It is the first time that this artificial intelligence method, known as Variational Auto Encoder (VAE), is being used to take inventory of insect biodiversity.
• Using an optical signal from an infrared sensor, the algorithm is able to decode insects flying through a light beam.