WaterWise (GaamaRamma)

Problem

Farmers waste or under-supply irrigation water because they cannot turn evapotranspiration and weather data into a daily plan.

Solution

A Next.js web app where a farmer draws a field on a map and enters crop, growth stage and irrigation efficiency; it combines OpenET evapotranspiration, Open-Meteo precipitation and crop coefficients into daily water needs, hours to run the system and field hydration status, with a GPT-3.5 chatbot for farming questions.

Demo moment

Not described in the sources.

Why it stood out

• organizer statedWon the award for making space data accessible or leveraging it for a unique application.Source ↗
• organizer statedAims to help farmers manage water availability in the face of drought, pests and disease.Source ↗
• team statedPresents actions rather than raw numbers: hydrated/dehydrated fields, pending irrigation jobs, hours to run tonight.Source ↗
• team statedShows its method: field area from polygon vertices with Earth curvature, then water use = (crop coefficient × ET − precipitation) × area.Source ↗
• curator inferenceThe data award went to the entry that turned one expert variable (evapotranspiration) into a single weekly decision.

Lessons for your next hackathon

• Pick one expert variable and turn it into one recurring decision (how much to water this week).
• Write the formula on the project page; judges of a data award can check it.
• The chatbot is an add-on; the schedule is the product.