big data

Oct 27, 2020

NC State, SAS, Microsoft to Predict Plant Disease and Tackle Food Recalls

The North Carolina Plant Sciences Initiative has teamed up with technology leaders SAS and Microsoft to predict plant disease and tackle food recalls. Several research teams will use cloud and internet of things (IoT) services from SAS and Microsoft for their interdisciplinary projects. 

May 27, 2020

Big Data for Better Sweetpotatoes

An interdisciplinary team led by Cranos Williams is setting out on a three-year project to use artificial intelligence to make sweetpotatoes even more profitable. The team will image hundreds of thousands of sweetpotatoes to increase the percentage of sweetpotatoes grown that are USDA grade 1. 

Apr 29, 2020

Plant Aid: A GRIP4PSI Big-Data Project to Detect Plant Diseases Faster

An interdisciplinary team led by professor Jean Ristaino will combine small sensors with big data for faster detection of the diseases plaguing tomato fields. From a hand-held plant disease ‘sniffer’ to a cloud-based database that can alert farmers about the cause of the stress and suggest possible mitigation strategies, the project aims to detect diseases early, improving yield. 

Dec 3, 2019

N.C. PSI, SAS to Harness the Power of Data

SAS will build off of its longstanding partnership with NC State and embed full-time data scientists within the N.C. Plant Sciences Initiative to collaborate on various research efforts ensure a safe and sustainable food supply. 

Aug 14, 2017

NC State and Oracle Pilot ‘Big Data’ Project to Grow Agriculture

Oracle has selected NC State as a university partner to pilot a new program that uses big data to solve grand challenges in everything from agriculture to textiles to engineering. First on the list? Sweet potatoes. 

May 9, 2017

Big Data Yields Plant Sciences Innovation

Computer scientists, data analytics experts and information technology professionals will play a role in the success of the North Carolina Plant Sciences Initiative. A recent workshop focused on how to improve the collection, analysis and applicability of agriculture’s big data.