Future Growers Technology

Mississippi State University (MSU) College of Ag. & Life Sciences and the Mississippi Agricultural & Forestry Experiment Station (MAFES) have entered into agreements with the U.S. Department of Agriculture (USDA) and two controlled environment agriculture (CEA) manufacturers to develop decision-support tools for academics, producers, and industry. A need for real-world modeling and analytical interfaces exists in academia and industry, whereby researchers, educators, and producers may simulate and train in CEA food production systems without risking animal or plant health.

Targeted in this project are greenhouse and animal production CEA systems. Partnering with Pulseworks® of Atlanta, GA (https://www.pulseworks.com), we aim to build readily-available CEA workforce training and research decision-support tools in both 2-D and 3-D formats. Software programs served worldwide across the internet will increase production in environmentally controlled conditions, including extraterrestrial food production.

Project outcomes will serve both:

  • 1) teaching communities, and
  • 2) research communities, whereby CEA virtual reality programs will
    • improve workforce development in environmentally controlled farming and,
    • provide agricultural researchers with the means to model and reverse engineer real-world production scenarios.
Our Mission: To increase food production in high-risk, high-reward environments

Theme


Creating real-world training and research decision-support tools for a robust environmentally controlled agriculture workforce under the project name: Future Grower Technologies®

Vision


We envision diverse education, research, and industry participation through integrating agriculture and computer sciences to build software-based, virtual reality training and decision-support tools that improve both greenhouse and animal production employment and research outcomes.

Goals


  • Design next-generation cyber learning approaches [web and program application program interfaces (API)] through high-risk, high-reward agricultural computing systems;
  • Increase the capacity of agricultural educators to advance student knowledge of production under CEA systems and, subsequently, increase the racial and gender diversity of market-ready graduates to enter related agriculture job markets;
  • Improve the prediction capability of agriculture researchers by providing decision-support tools for modeling forward- and backward-production scenarios;
  • Develop an ethnically and gender diverse computer science-agriculture graduate-level technology incubator for engineering software solutions that address data quality, security, and training needs; and
  • Advance the development of a stochastic decision-support system for modeling production scenarios.