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I-Corps: Data Analytics for Hand-Picked Agriculture

Sowers, Richard
University of Illinois - Urbana-Champaign
Start date
End date
The broader impact/commercial potential of this I-Corps project is to improve food safety and security of hand harvested food crops through individualized geo-spatial data collection. Much like highway cameras have an ability to tell the status of traffic on a given freeway are vastly improved through the individualized data of each driver and their phones, hand harvested food can benefit from improving traceability and transparency from block level data to single points of harvest, recalling intersecting mapping data. Geo-specific data can also enable better land productivity measurement over textual block data. Individualized, personally owned, data enables workers to have portability of information, building a resume of dependability and performance. In addition, it becomes an integral component of a viable transparency scheme. The project will potentially improve the economic standing of harvesters, improve the capability of farmers to better understand their workforce and land, assist marketers with improved data to insure against breakdowns in food safety, and enable consumers transparency without added steps to the system.

This I-Corps project further develops improved data collection methods in high-value hand-picked specialty crops. The data in question is a precise record of who harvests what, where and when. Contextual data (quality, irrigation/fertilization data, cultural practice maps, etc.) are added and visualized for farmers. The project will test the relationship of data sets toward improved decision skills; better management depends on better measurement. It provides a better understanding of inhomogeneities in space and time. Addressing these inhomogeneities leading to investigation and reductions of variance. The system will enables comprehension of bottlenecks and workflow problems in labor process and allow dynamic and real-time decisions. The system will also allow quantitative approaches toward finding optimal decisions in several parts of the system and provide a platform for decentralized decision-making based on common data.
Funding Source
United States Nat'l. Science Fndn.
Project source
View this project
Project number
Bacterial Pathogens
Natural Toxins
Chemical Contaminants