Drones and AI have learned to determine the maturity of the crop with high accuracy

University of Illinois scientists predict soybean maturity in two days using

drone images and artificial intelligence. This reduces the need for manual plant selection, which is not only slower and more tedious for breeders, but also prone to error.

“Assessing pod maturity takes a lottime and error prone. It's a pod color-based grading system, so it's human-driven, '' explains Nicholas Martin, assistant professor of crop production in Illinois and co-author of the study. “Many research groups are trying to use drone imagery to assess maturity, but not scale up and automate the process. We came up with a more accurate way to do it. "

Rodrigo Trevisan, a doctoral student working withMartin, trained computers to detect changes in pod color from drone images collected over five trials, three growing seasons in two countries.

The expert used deep convolutional neuralnetworks (CNN) - a type of artificial intelligence. CNN's work is similar to how the human brain learns to interpret the components of images - color, shape, texture - received from human eyes.

The new method of analysis will allow breeders to make decisions about the fate of crops on a larger scale than ever before, the scientists conclude.

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