Miscanthos is one of the most promising perennial crops for bioenergy production, as it is able to produce high yields with a small environmental footprint. This versatile grass has great potential to perform even better because far less effort has gone into improving it through breeding than established staple crops like corn or soybeans.
However, breeding must become faster and more efficient if it is to realize the potential for sustainable and resilient biomass production in miscanthus. A key bottleneck in the process is the ability to measure the growth of thousands of varieties of the crop in the field and select the small number of varieties that perform best. This requires new, sophisticated technologies for data collection and analysis.
A study by researchers at the Center for Advanced Bioenergy and Bioproducts Innovation (CABBI) has shown how unmanned aerial vehicles (UAVs, or drones) combined with cutting-edge machine learning methods can help select the best candidate genotypes in miscanthus breeding programs. The team used neural networks—computer systems modeled after the human brain and nervous system—to analyze very high-resolution aerial images and identify key features of miscanthus during the crop’s growing season.
In particular, the CABBI researchers highlighted that the use of neural networks designed to analyze data in three dimensions (two dimensions in space, plus time) enabled better estimates of crop traits (flowering time, height and biomass production) than traditional neural networks. which analyze data in only two dimensions in space. This allowed them to harness information about how each of the thousands of plants in the field changes over time. Additionally, the 3D neural network was shown to be able to automatically perform aspects of the image analysis process (i.e. finding plants in the image) that in many other cases require substantial manual intervention that would slow down the process.
This is particularly important in high-yielding perennial grasses such as miscanthus, where field phenotyping is more demanding as well as more rewarding.
The study, published in remote sensing, It was led by Postdoctoral Researcher Sebastian Varela at CABBI, a bioenergy research center funded by the US Department of Energy. Andrew Leakey, CABBI Director, Professor and Head of the Department of Plant Biology and Professor at the Carl R. Woese Institute for Genomic Biology (IGB), Department of Crop Sciences and Center for Digital Agriculture at the University of Illinois Urbana-Plain; and Erik Sacks, CABBI Deputy Theme Leader for Feedstock Production and Professor of Crop Sciences and IGB at Illinois.
It was the first attempt to use data-intensive monitoring of large, genetically diverse populations of miscanthus using digital technologies. For their evaluation, the researchers used drones to take high-resolution images of crops 10 times during the growing season, along with soil data for thousands of miscanthus genotypes, to determine flowering time, height and biomass yield. The imaging combined photogrammetry, which provides digital models of surfaces, and multispectral sensing technology that can capture images not visible to the human eye.
“This is an exciting step toward developing digital applications that can facilitate the selection of the best candidate genotypes for a fraction of the cost of traditional manual screening,” Leakey said. “This is just one key step in the larger work CABBI is doing to provide the scientific understanding and technological advances needed to make environmentally beneficial and profitable bioenergy a reality for the Central US.”
Said Sacks: “Our standard methods for measuring miscanthus traits such as yield and height are time-consuming and labor-intensive, but these new imaging methods are faster and much less expensive. With the newer methods, we can we can evaluate larger populations of miscanthus for the same money – and this will allow us to select better breeding lines and varieties faster.”
Co-authors on the study included Ph.D. student Xuying Zheng, undergraduate Dylan P. Allen, and research technician Jeremy Ruhter, all with CABBI and Crop Sciences. and Ph.D. student Joyce N. Njuguna of Crop Sciences.
Sebastian Varela et al, Deep Convolutional Neural Networks Exploit High Spatial and Temporal Resolution Aerial Images to Identify Key Phenotype Characteristics in Miscanthus, Remote sensing (2022). DOI: 10.3390/rs14215333
Provided by the University of Illinois at Urbana-Champaign
Reference: Team adds powerful new dimension to next-generation bioenergy phenotyping (2022, November 4) Retrieved November 5, 2022 from https://phys.org/news/2022-11-team-powerful-dimension-phenotyping- next-gen. html
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