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.

Researchers launch and retrieve drones from a boat to photograph humpback and minke whales in the Western Antarctic Peninsula. BEAUFORT -- The swift pace of technological development has given researchers tools that can collect more data in less time and with fewer resources than a decade ago. Lightweight tags with long-lasting batteries can track animals as small as insects and measure the conditions around them. DNA sequencing technologies have decoded the genomes of thousands of organisms from the loblolly pine to the black bear. Drones can quickly photograph landscapes and animals in locations that may be inaccessible or unsafe for people.

An aerial agricultural service company called Airviz, started by Bryan Johnston and Alex Dodds, plans to fly drone over farm fields in Canada and aims to provide instant feedback on the crop condition. "Our business plan is we fly it (an agricultural field) frequently throughout the season and give you immediate results," said Johnston. The quick results are only possible because of the involvement of drone technology. "The new technology means that drone services companies can provide rapid information and growers can act on that information immediately," said Matthew Johnson, owner of M3 Aerial Productions in Manitoba. "My five-year plan is to make both hardware and software that will be able to do this completely autonomously, with no pilot needed whatsoever," said Alex Dodds.

I met with a team of Microsoft AI researchers recently to discuss original adaptations of Resnet 50, a version of the convolutional network Microsoft used to win the Imagenet 2015 image recognition competition. The discussion about the scientists work caused me to reconsider the inextricable link between IoT and machine learning. Control loops are a fundamental principal of the internet of things (IoT.) If then, then that (ITTT) has a long history in conditionally controlling things dating to the invention of the electric relay in the 1830s. Over time, single relays were combined into state machines, and later, relays became transistors.

Patrick Meier (center, in cap) flies a drone in Nepal after the earthquake in 2015. Meier and his team were able to to capture detailed images of damage around the capital, Kathmandu. He believes using this technology will make crisis mapping even more effective for disaster response. Patrick Meier (center, in cap) flies a drone in Nepal after the earthquake in 2015. Meier and his team were able to to capture detailed images of damage around the capital, Kathmandu. He believes using this technology will make crisis mapping even more effective for disaster response.

Drone images accumulate much faster than they can be analyzed. Researchers have developed a new approach that combines crowdsourcing and machine learning to speed up the process. Who would win in a real-life game of "Where's Waldo," humans or computers? A recent study suggests that when speed and accuracy are critical, an approach combing both human and machine intelligence would take the prize. With drones being used to monitor everything natural disaster sites, pollution, or wildlife populations, analyzing drone images in real-time has become a critically important big data challenge. Publishing in the journal Big Data, researchers, including Stéphane Joost from EPFL, present a new approach to rapidly interpret aerial images taken by camera drones that combines human crowdsourcing and machine learning.