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MSU Plant Sensors and Analytics
Platforms: The internet of plants!
David M. Kramer
MSU-DOE Plant Research Lab
Renfu Lu
Bio...
Yield prediction
Management (precision ag)
Genes for breeding
New sensors
Modeling
OLIVER
1O2
O2
Dy
Rapid light fluctuatio...
Computer Vision for Plants
Xi Yin, Xiaoming Liu, Jin Chen, David M. Kramer, “Multi-leaf Alignment from Fluorescence Plant ...
Sensing and Automation for Specialty Crop Production
Sensing and Automation for Specialty Crop Production
3-D Imaging and Sensing for Fruit Yield/Quality Mapping
• Number, siz...
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The Internet of Plants

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David M. Kramer, Renfu Lu, and Xioming Lu present their research on using sensors and automated technology to determine agricultural analytics.

Veröffentlicht in: Ingenieurwesen
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The Internet of Plants

  1. 1. MSU Plant Sensors and Analytics Platforms: The internet of plants! David M. Kramer MSU-DOE Plant Research Lab Renfu Lu Biosystems and Agricultural Engineering & USDA/ARS Xiaoming Liu Computer Science and Engineering
  2. 2. Yield prediction Management (precision ag) Genes for breeding New sensors Modeling OLIVER 1O2 O2 Dy Rapid light fluctuations Scientific insights. Mechanisms and genes Dynamic responses of photosynthesis DEPI: Dynamic Environmental Phenotype Imager OLIVER: “Big phenotype Analytics Controlled field simulations Gene mapping MultispeQ Deep learning
  3. 3. Computer Vision for Plants Xi Yin, Xiaoming Liu, Jin Chen, David M. Kramer, “Multi-leaf Alignment from Fluorescence Plant Images,” in Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV 2014), Steamboat Springs, CO, March 24-26, 2014. (Best Student Paper Award) Yousef Atoum, Muhammad Jamal Afridi, Xiaoming Liu, J. Mitchell McGrath, Linda E. Hanson, "On Developing and Enhancing Plant-Level Disease Rating Systems in Real Fields," Pattern Recognition, Dec. 2015
  4. 4. Sensing and Automation for Specialty Crop Production
  5. 5. Sensing and Automation for Specialty Crop Production 3-D Imaging and Sensing for Fruit Yield/Quality Mapping • Number, size and spatial distribution of fruit • Maturity (appearance, SSC, firmness, etc.) Harvest-Assist Automatic Handling Sorting/Grading (color, size, defect) Quality/Yield Mapping GPS, imagery Sensors for worker productivity monitoring and optimization Storage/Warehouse Tracking Machine vision Apple Harvest and Automated Infield Sorting Technology Structured-illumination Reflectance Imaging for Food Quality Detection

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