Utilizing AI/ML and Satellite Imagery to Increase Water Efficiency for Rice Farming

Challenge Background

According to World Bank Group, The Philippines is one of the most natural hazard-prone countries in the world. The social and economic cost of natural disasters in the country is increasing due to population growth, change in land-use patterns, migration, unplanned urbanization, environmental degradation, and global climate change. Agriculture, specifically rice farms which are one of the most important crops in the Philippines are no exception to climate change such as soil nutrition deficiency that could lead to wide food insecurity for the next decades.

The Problem

The goal of the project is to build an open-source AI-driven interactive map that predicts the required water for a rice farm. The tool will be a low-cost alternative for expensive sensors to aid rice farmers to consume water irrigation efficiently.

Having the tool available to rice farmers and other related stakeholders will help them save an irrigation cost and adapt to the impacts of climate change.

Goal of the Project

• Development of a machine learning model.
• Web App containing Map of the sites.
• GitHub Repo with open source code.
• Curated dataset hosted in AWS or Google for open access.

Project Timeline

What you'll learn