In a recent paper published in Frontiers in Plant Science, Purdue University geomatics PhD candidate Claudia Aviles Toledo, working with her faculty advisors and co-authors Melba Crawford and Mitch Tuinstra, demonstrated the capability of a recurrent neural network—a model that teaches computers to process data using long short-term memory—to predict maize yield from several remote sensing technologies and environmental and genetic data.

Plant phenotyping can be a labour-intensive task where plant characteristics are examined and characterized. Measuring plant height by tape measure, gauging reflected light over multiple wavelengths using heavy handheld equipment, and pulling and drying individual plants for chemical analysis are all labour-intensive and expensive efforts. Remote sensing, or gathering these data points from a distance using uncrewed aerial vehicles (UAVs) and satellites, makes such field and plant information more accessible.

Tuinstra, the Wickersham Chair of Excellence in Agricultural Research, professor of plant breeding and genetics in the Department of Agronomy and the science director for Purdue's Institute for Plant Sciences, said, "This study highlights how advances in UAV-based data acquisition and processing coupled with deep-learning networks can contribute to the prediction of complex traits in food crops like maize."

Crawford, the Nancy Uridil and Francis Bossu Distinguished Professor in Civil Engineering and a professor of agronomy, gives credit to Aviles Toledo and others who collected phenotypic data in the field and with remote sensing. Under this collaboration and similar studies, the world has seen remote sensing-based phenotyping simultaneously reduce labour requirements and collect novel information on plants that human senses alone cannot discern.

Geometric structure

Hyperspectral cameras, which make detailed reflectance measurements of light wavelengths outside the visible spectrum, can now be placed on robots and UAVs. Light Detection and Ranging (LiDAR) instruments release laser pulses and measure the time when they reflect the sensor to generate maps called "point clouds" of the geometric structure of plants.

"Plants tell a story for themselves," Crawford said. "They react if they are stressed. If they react, you can potentially relate that to traits, environmental inputs, management practices such as fertilizer applications, irrigation or pests."

As engineers, Aviles Toledo and Crawford build algorithms that acquire massive datasets and analyze their patterns to predict the statistical likelihood of different outcomes, including the yield of different hybrids developed by plant breeders like Tuinstra. These algorithms categorize healthy and stressed crops before any farmer or scout can spot a difference, and they provide information on the effectiveness of different management practices.

Deep Learning Model

Tuinstra brings a biological mindset to the study. Plant breeders use data to identify genes controlling specific crop traits.

"This is one of the first AI models to add plant genetics to the story of yield in large, multiyear plot-scale experiments," Tuinstra said. "Now, plant breeders can see how different traits react to varying conditions, which will help them select traits for future more resilient varieties. Growers can also use this to see which varieties might do best in their region."

This neural network was built using remote-sensing hyperspectral and LiDAR data from corn, genetic markers of popular corn varieties, and environmental data from weather stations. This deep-learning model is a subset of AI that learns from spatial and temporal patterns of data and makes future predictions. Once trained in one location or time period, the network can be updated with limited training data in another geographic location or time, thus limiting the need for reference data.

Crawford said, "Before, we used classical machine learning, which focused on statistics and mathematics. We couldn't use neural networks because we didn't have the computational power."

Working with Tuinstra, Crawford's long-term goal is to incorporate genetic markers more meaningfully into the neural network and add more complex traits to their dataset. Accomplishing this will reduce labour costs while providing growers the information to make the best decisions for their crops and land.

Phys Org