Drones and AI detect soybean maturity with high accuracy

December 07, 2020

URBANA, Ill. - Walking rows of soybeans in the mid-summer heat is an exhausting but essential chore in breeding new cultivars. Researchers brave the heat daily during crucial parts of the growing season to look for plants showing desirable traits, such as early pod maturity. But without a way to automate detection of these traits, breeders can't test as many plots as they'd like in a given year, elongating the time it takes to bring new cultivars to market.

In a new study from the University of Illinois, researchers predict soybean maturity date within two days using drone images and artificial intelligence, greatly reducing the need for boots on the ground.

"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."

Rodrigo Trevisan, a doctoral student working with Martin, trained computers to detect changes in canopy color from drone images collected across five trials, three growing seasons, and two countries. Importantly, he was able to account for "bad" images to maintain accuracy.

"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," Trevisan 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."

Trevisan used a type of artificial intelligence called deep convolutional neural networks. He says CNNs are similar to the way human brains learn to interpret components of images - color, shape, texture - 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."

Martin says commercial breeding companies are clamoring for these capabilities.

"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. "Finding a good method to help breeders actually make decisions on large scales is quite exciting."
-end-
The article, "High-throughput phenotyping of soybean maturity using time series UAV imagery and convolutional neural networks," is published in Remote Sensing [DOI: 10.3390/rs12213617]. Authors include Rodrigo Trevisan, Osvaldo Perez, Nathan Schmitz, Brian Diers, and Nicolas Martin.

The Department of Crop Sciences is in the College of Agricultural, Consumer and Environmental Sciences at the University of Illinois.

University of Illinois College of Agricultural, Consumer and Environmental Sciences

Related Artificial Intelligence Articles from Brightsurf:

Physics can assist with key challenges in artificial intelligence
Two challenges in the field of artificial intelligence have been solved by adopting a physical concept introduced a century ago to describe the formation of a magnet during a process of iron bulk cooling.

A survey on artificial intelligence in chest imaging of COVID-19
Announcing a new article publication for BIO Integration journal. In this review article the authors consider the application of artificial intelligence imaging analysis methods for COVID-19 clinical diagnosis.

Using artificial intelligence can improve pregnant women's health
Disorders such as congenital heart birth defects or macrosomia, gestational diabetes and preterm birth can be detected earlier when artificial intelligence is used.

Artificial intelligence (AI)-aided disease prediction
Artificial Intelligence (AI)-aided Disease Prediction https://doi.org/10.15212/bioi-2020-0017 Announcing a new article publication for BIO Integration journal.

Artificial intelligence dives into thousands of WW2 photographs
In a new international cross disciplinary study, researchers from Aarhus University, Denmark and Tampere University, Finland have used artificial intelligence to analyse large amounts of historical photos from WW2.

Applying artificial intelligence to science education
A new review published in the Journal of Research in Science Teaching highlights the potential of machine learning--a subset of artificial intelligence--in science education.

New roles for clinicians in the age of artificial intelligence
New Roles for Clinicians in the Age of Artificial Intelligence https://doi.org/10.15212/bioi-2020-0014 Announcing a new article publication for BIO Integration journal.

Artificial intelligence aids gene activation discovery
Scientists have long known that human genes are activated through instructions delivered by the precise order of our DNA.

Artificial intelligence recognizes deteriorating photoreceptors
A software based on artificial intelligence (AI), which was developed by researchers at the Eye Clinic of the University Hospital Bonn, Stanford University and University of Utah, enables the precise assessment of the progression of geographic atrophy (GA), a disease of the light sensitive retina caused by age-related macular degeneration (AMD).

Classifying galaxies with artificial intelligence
Astronomers have applied artificial intelligence (AI) to ultra-wide field-of-view images of the distant Universe captured by the Subaru Telescope, and have achieved a very high accuracy for finding and classifying spiral galaxies in those images.

Read More: Artificial Intelligence News and Artificial Intelligence Current Events
Brightsurf.com is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to Amazon.com.