Using drones and AI to detect soybean maturity with high accuracy

Checking on soybean maturity manually is an arduous and labor-intense process. However, researchers from the University of Illinois can predict the soybean maturity date within a two-day-range using drone images and artificial intelligence.

Using drones and AI to detect soybean maturity with high accuracy

“Assessing pod maturity is very time consuming and prone to errors. It’s a scoring system based on the color of the pod, so it is also subject to human bias,” says Nicolas Martin, assistant professor in the Department of Crop Sciences at Illinois and co-author on the study. “Many research groups are trying to use drone pictures to assess maturity, but can’t do it at scale. So we came up with a more precise way to do that. It was really cool, actually.”

The computers were trained to recognize variations in canopy color from drone photos that were collected over five trials, during three seasons in two different countries.

“Let’s say we want to collect images every three days, but one day, there are clouds or it’s raining, so we cannot. In the end, when you get the data from different years or different locations, they will all look different in terms of the number of images and the intervals and so on,” Rodrigo Trevisan, a doctoral student working with Martin says. “The main innovation we developed is how we can account for whatever we are able to collect. Our model performs well independent of how often the data was collected.”

A type of artificial intelligence was used that uses deep convolutional neural networks (CNNs) which learns in a way that is very similar to how human learn with information we received from our eyes.

“CNNs detect slight variations in color in addition to shapes, borders, and texture. For what we were trying to do, color was the most important thing,” Trevisan says. “But the advantage of the artificial intelligence models we used is that it would be quite straightforward to use the same model to predict another trait, such as yield or lodging. So now that we have these models set up, it should be much easier for people to use the same architecture and the same strategy to do many more things.”

According to Martin, growing companies are clamoring for capabilities like these.

“We had industry partners on the study who definitely want to use this in the years to come. And they made very good, important contributions. They wanted to make sure the answers were relevant for breeders in the field making decisions, selecting plants, and for farmers,” Martin says according to the Journal Gazette. “Finding a good method to help breeders actually make decisions on large scales is quite exciting.”

drones and AI make a powerful combination when it comes to monitoring and inspecting crops, installations, solar farms, and more. Read more about how unmanned aircraft systems (UAS) and artificial intelligence are used here.

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