Title:
Developing Sensing and Robotics Technologies for Plant Phenomics
Developing Sensing and Robotics Technologies for Plant Phenomics
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Author(s)
Li, Changying
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Abstract
Feeding a world population of 9 billion people by
2050 amidst increasing climate variability is one of
the greatest challenges facing humanity. The
genomics revolution provides unprecedented power
to develop new and advanced crop cultivars with the
gene combinations needed to address these global
challenges. However, rapid and repeatable
measurement of crop phenotypic parameters
remains a major bottleneck in plant genomics and
breeding programs. High-throughput phenotyping
technologies that can quickly and repeatedly scan
tens of thousands of individuals using an array of
advanced sensor and data analytics tools are critical
to improving the ability of scientists to dissect the genetics of quantitative traits such as yield and
stress tolerance. Our interdisciplinary team is developing a robot-assisted
field-based high throughput phenotyping
system that integrates both ground and unmanned
aerial elements to quantitatively measure a suite of
key traits iteratively throughout the growing season.
This project is expected to unmask plant responses
that will inform a new level and quality of decision-making
in the selection of crop genotypes for
specific production conditions. A deep learning
convolutional neural network was used to identify
flowers and the flowering peak of cotton plants. The
task coordination between ground and aerial
vehicles will result in new discoveries in the area of
partitioning and coverage control. In a different but related project, the talk will
introduce a Berry Impact Recording Device (BIRD)
sensor that has been successfully developed to
simulate a real berry to quantitatively measure
mechanical impacts on the real fruit created by
machine harvesters, packing lines, and
transportation vehicles.
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Date Issued
2017-10-25
Extent
58:25 minutes
Resource Type
Moving Image
Resource Subtype
Lecture