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

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. 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. 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. Sensing and Automation for Specialty Crop Production

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