Using Touch Sensitive Soft Robots to Improve Vineyard Management
Jul 6, 2017
This project aims to develop an automated vineyard system to accurately determine vine yield, leaf area to fruit ratio, and cluster integrity based on touch-sensitive soft robots (rather than the industry standard of computer vision), accurately estimating grape vine yields prior to harvest. Researchers: Kirstin Petersen and Justine Vanden Heuvel.
Using Micro Water Sensor to Manage Irrigation in Apple Orchards
Jul 6, 2017
This project is developing an integrated digital solution for optimizing irrigation in apple orchards, using a micro water stress sensor technology - the microtensiometer - recently developed by Cornell. This tiny and inexpensive chip combined with smart technology systems collecting and interpreting the data it provides, will serve as a foundation for the next generation of automated irrigation systems, delivering water only where it is needed. Researcher: Abraham Stroock.
E-synch: Improving Cattle Reproductive Management
Jul 5, 2017
The easy-to-use E-sunch device will reduce the hassle of reproductive management of dairy cows, using sensors to closely monitor individual animals and customize the protocols for their reproductive cycle in real-time. An electronically controlled, reusable device will deliver reproductive hormones automatically. This project aims to help balance and optimize the productivity and health of millions of cows around the world. Researcher: Julio Giordano.
What Keeps Farmers from Adopting Digital Agriculture?
Jul 4, 2017
This study on social and policy considerations aims to uncover the concerns farmers might have about adopting new technologies, such as the implications of the novel forms of collecting and processing huge amounts of data, and how this new data flow from seed to super market might impact existing economic relationships. The study explores, if the industry sufficiently addresses those underlying concerns of farmers. Researcher: Solon Barocas.
Advancing Digital Agriculture and Conservation Through Data Driven Decisions
Jul 3, 2017
The connections between agriculture and sustainability are complex, and so are the ever-increasing streams of available data. Making use of all this data in efficient ways remains a challenge. This project aims to combine and adopt data-driven optimization and advanced machine learning techniques with digital agriculture data. Advanced analytics are needed, to inform conservation decision-making both at the farm and policy level. Researcher: Fengqi You.
Facilitating Access to Complex Climate and Weather Data
Jun 30, 2017
The Northeast Regional Climate Center (NRCC) has developed the Applied Climate Information System (ACIS) that allows users to easily access a wide range of weather observations, climate projections and weather forecasts. ACIS provides researchers and programmers with webservice data access and facilitates the development of an array of stakeholder-driven decision tools and products by NRCC staff. Researcher: Arthur DeGaetano. More information
Intelligent Lighting Systems in Greenhouses
Mar 21, 2017
The Greenhouse Lighting and Systems Engineering (GLASE) consortium is advancing LED light engineering, plant photobiology, carbon dioxide enrichment and systems control to create intelligent systems that can dramatically reduce the energy cost and carbon footprint of horticultural lighting. Researcher: Neil Mattson. Read more
Using Drone NDVI Imaging to Manage Nitrogen and Yield
Mar 20, 2017
This project is evaluating the use of drone-generated NDVI (normalized difference vegetation index) maps as tools for predicting yield and nitrogen needs of corn and forage sorghum. The team is developing a standard operating procedure for using drones to collect NDVI imagery to ensure consistent, actionable data under changing light and growing condition. Researchers: Quirine Ketterings and Elson Shields. More information
Leaf Doctor and Estimate, two new free apps, work with photo analysis of damaged leaves to determine plant disease severity and help growers and researchers to decide if and how to treat the plant. While Leaf Doctor analyzes photos users take, Estimate connects users to a database of diseased leaves to help determine damage. Researcher: Sarah Pethybridge