Collaborative Research: CPS: Medium: Secure CPS for Real-time Agro-analytics
Lead PI:
Aniket Kate
Abstract
Cyber-physical systems (CPS) have now started to play an increasingly important role in autonomous sensing, analysis, and tasking in a variety of agricultural settings ranging from sustainable farming to livestock monitoring. Many of these settings demand real-time analytics, at varying timescales, and the CPS devices have to coordinate among themselves over a variety of wireless networks.
Performance Period: 02/15/2021 - 02/14/2024
Institution: Purdue University
Sponsor: USDA
Award Number: 2038986
Collaborative Research: CPS: Medium: Early Stage Plant Disease Detection via Robotic Sampling and on site Metagenomic Sequencing
Lead PI:
Song Li
Abstract
Plant diseases pose an increasing threat to the nation's food supply and biosecurity in a changing environment. There is a growing risk of plant pathogens spreading through shipments of stock plants between production facilities, and from production facilities to growers or to retailers and consumers. Recent failures to prevent plant disease emergence and spread in the US has resulted in major economic losses (20-40% of total yield) for growers.
Performance Period: 01/15/2021 - 01/14/2025
Institution: Virginia Polytechnic Institute and State University
Sponsor: USDA
Award Number: 1025458
CPS: Medium: Collaborative Research: Using Computer Vision to Improve Data Input for Precision Thinning Models in Apples
Lead PI:
Daniel Cooley
Abstract
This has the specific goal of significantly shortening the time to measure apple fruitlet growth, which will enable apple growers to manage crop load more precisely and efficiently. The technology developments used to do this will make fundamental advances in robotics and sensing for agriculture, and will take important steps toward using intelligent robots as labor saving devices in specialty crop production. The project will train graduate and undergraduate students in plant science and computer science, giving them important cross-disciplinary research experiences early in their careers.
Performance Period: 06/01/2020 - 05/31/2024
Institution: University of Massachusetts Amherst
Sponsor: USDA
Award Number: 1931913
CPS: Medium: Collaborative Research: Using Computer Vision to Improve Data Input for Precision Thinning Models in Apples
Lead PI:
George Kantor
Abstract
Commercial apple growers must remove a portion of new apple fruitlets every year in order maintain fruit size, and prevent trees from develop a pattern of alternative year bearing, where some years there are many fruit, and other years very few. The challenge for a commercial grower is to remove a number fruitlets in the first three to four weeks after they form so as to optimize size and number of harvested fruit later in the year. To do this, commercial growers spray chemicals, generally two to four applications per year.
Performance Period: 06/01/2020 - 05/31/2024
Institution: Carnegie Mellon University
Sponsor: USDA
Award Number: 1932498
CPS: TTP Option: Medium: DATAg: FieldDock: An Integrated Smart Farm Platform for Real-Time Agronomic Optimization and Accelerated Crop Breeding
Lead PI:
Nadia Shakoor
Abstract
High throughput field phenotyping is a relatively new but rapidly growing research area, and it will remain a top agricultural research priority in the next decade. Remote sensing technologies, proximal sensors, platforms such as unmanned aerial vehicles (UAVs) and ground vehicles, and statistical data-driven analytics are being rapidly customized and deployed for high throughput phenotyping and used as plant performance measurement tools for crop improvement/breeding and precision agriculture systems for agronomy, soil science, and farm management.
Performance Period: 06/15/2020 - 06/14/2024
Institution: Donald Danforth Plant Science Center
Sponsor: USDA
Award Number: 1932569
CPS: Medium: Collaborative Research: Field-scale, single plant-resolution agricultural management using coupled molecular and macro sensing and multi-scale data fusion and modeling
Liang Dong
Lead PI:
Liang Dong
Co-PI:
Abstract
Water and nitrogen represent two of the most expensive inputs to agricultural systems, and two of the critical constraints on overall agricultural productivity. Today, farmers generally over apply nitrogen fertilizer, because the potential cost of over application is less than the potential cost of achieving suboptimal yields. Similarly, in farm settings water is often over applied, particularly when studies are conducted at high resolution within individual center-pivot fields.
Liang Dong

Liang Dong is an associate professor of electrical and computer engineering at Baylor University. His research interests include Digital Communications and Signal Processing, Green Wireless Networks, Cyber-Physical System and Security, Social Internet of Things, and E-health Applications.

Liang Dong is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), a member of the American Physical Society (APS), and a member of the American Society for Engineering Education (ASEE). He served on the executive board of IEEE West Michigan Section from 2006 to 2011 and the executive board of ASEE North Central Section from 2007 to 2008. He also served as a TPC member for IEEE HealthCom 2015, IEEE GlobalSIP 2015 and IEEE GlobalSIP 2016, and a session chair for IEEE WCNC 2013 and IEEE GlobalSIP 2016. He is a member of Sigma Xi, Phi Kappa Phi, and Tau Beta Pi, and a faculty advisor of Eta Kappa Nu.

Performance Period: 06/01/2020 - 05/31/2023
Institution: Iowa State University
Sponsor: USDA
Award Number: 1932428
CPS: Medium: Collaborative Research: Field-scale, single plant-resolution agricultural management using coupled molecular and macro sensing and multi-scale data fusion and modeling
Liang Dong
Lead PI:
Liang Dong
Abstract
Water and nitrogen represent two of the most expensive inputs to agricultural systems, and two of the critical constraints on overall agricultural productivity. Today, farmers generally over apply nitrogen fertilizer, because the potential cost of over application is less than the potential cost of achieving suboptimal yields. Similarly, in farm settings water is often over applied, particularly when studies are conducted at high resolution within individual center-pivot fields.
Liang Dong

Liang Dong is an associate professor of electrical and computer engineering at Baylor University. His research interests include Digital Communications and Signal Processing, Green Wireless Networks, Cyber-Physical System and Security, Social Internet of Things, and E-health Applications.

Liang Dong is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), a member of the American Physical Society (APS), and a member of the American Society for Engineering Education (ASEE). He served on the executive board of IEEE West Michigan Section from 2006 to 2011 and the executive board of ASEE North Central Section from 2007 to 2008. He also served as a TPC member for IEEE HealthCom 2015, IEEE GlobalSIP 2015 and IEEE GlobalSIP 2016, and a session chair for IEEE WCNC 2013 and IEEE GlobalSIP 2016. He is a member of Sigma Xi, Phi Kappa Phi, and Tau Beta Pi, and a faculty advisor of Eta Kappa Nu.

Performance Period: 06/01/2020 - 05/31/2023
Institution: Iowa State University
Sponsor: USDA
Award Number: 1932554
CPS: Medium: Collaborative Research: Robust and Intelligent Optimization of Controlled-environment Agriculture System for Food Productivity and Nutritional Security
Lead PI:
George Lan
Abstract
Harnessing recent progresses on wastewater treatment, food security, data analytics, and machine intelligence, we propose to study novel optimized technology-driven controlled-environment agriculture (CEA) systems that can achieve high areal vegetable productivity to increase the food and nutritional security in urban areas with low operating cost and reduced energy consumption.Our project focuses on two core CPS research areas, i.e., control and data analytics, inspired by the design and operations of a pilot testbed at Georgia Tech for coupling the water and nutrients in domestic wastewater
Performance Period: 06/01/2020 - 05/31/2024
Institution: Georgia Institute of Technology
Sponsor: USDA
Award Number: 1931919
CPS: Small: Learning to Pick Fruit Using Closed Loop Control and In hand Sensors
Lead PI:
Joseph Davidson
Abstract
The goal of this project is to use proprioception, localized sensing, and observed forces to develop robust, autonomous fruit picking methods. Fresh market tree fruit growers still rely on a large seasonal labor force for harvesting operations. Despite extensive research over the past thirty years, robotic harvesters are not yet commercially available. Prior work has considered manipulation a robot position control problem, disregarding the need for sensor input after physical contact with the fruit.
Performance Period: 06/01/2020 - 05/31/2024
Institution: Oregon State University
Sponsor: USDA
Award Number: 1932205
CAREER: Situational Awareness Strategies for Autonomous Systems in Dynamic Uncertain Environments
Lead PI:
Zak Kassas
Abstract

The potential economic and societal impacts of realizing fully autonomous cyber-physical systems (CPS) are astounding. If the Federal Aviation Administration (FAA) allows integration of unmanned aerial vehicles (UAVs) into the national civilian airspace, the private-sector drone industry is estimated to generate more than 100K high-paying technical jobs over a ten-year span and contribute $82B to the U.S. economy.

Performance Period: 10/01/2022 - 03/31/2024
Institution: Ohio State University
Sponsor: NSF
Award Number: 2240512
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