👤 Ethan Kang, Teddy Smith, Pedro Dal Bo, Manav Shah, Justin Hopf

Team 12

Problem Space

Within the next few years, global demands will continue to rise, as the impacts of climate change strengthens and global food demands continue to rise. Without improving the way we grow our crops, the agricultural industry cannot continue to sustainably feed the world.

Rural agricultural areas, especially small farms, suffer from limited access to capital and infrastructure. With this, they lack access to critical information about their farm that may inhibit efficiencies. Although technology is available through forms of precision agriculture, this often requires significant initial capital and is not feasible at smaller scales. Small farms are scared use a technology that has slow implementation time with large costs.

Furthermore, smaller farms have significant distrust of allowing technology influence their farming decisions.

Therefore, our primary target audience is small local farms who have little to no technology implementations, with a hope to expand this into third-world countries.

A viable solution lies in improving the resiliency and adaptability of these farms, while providing key data metrics that helps farmers make key decisions.

How we know this is a legitimate problem - Pedro

• Conducted user interviews
  • "Gut feeling" analytics
  • Used one sensor at closest location to "confirm" assumptions

Solution/Experiment (Physical solution)

• talk about solution (two pronged)
  • Physical solution (robot)
    • show technical drafts
      • e.g. sketches or solidworks designs

Digital Solution - Justin

• use of publically available data (APIs)
  • primary data source is from robot deployed to local region
  • combine with existing models to personalize data
  • established models and information
  • weather data, soil moisture, light intensity

Business Model

• subscription based
  • variable costs + initial costs distributed over time
• Model exemplifies drive for low entrance cost

57013, 53093