Pest Prediction
Turning data into actionable insights for farmers
THE
PROBLEM
Farmers often struggle to predict pest outbreaks, leading to crop damage and financial loss. The NASA Space Apps Challenge asked us to build a solution that could provide actionable, data-driven predictions locally in Turin, with the potential for broader adoption.
OUR
APPROACH
We built a machine learning platform that analyzes historical crop and pest data to predict outbreaks before they happen. The platform includes a dashboard for farmers, visualizing risk levels, recommended actions, and real-time alerts. Our solution was designed to be scalable, reliable, and easy to use in real-world agricultural settings.
Predictive Dashboard for Farmers
Incubation Opportunity at i3p
Team Presentation at NASA Space Apps Challenge
Local Testing with Farms in Turin
IMPACT
PROCESS
DATA COLLECTION
Compiled historical crop and pest datasets relevant to the region
MODEL DEVELOPMENT
Built predictive ML models to forecast pest outbreaks with high accuracy
DASHBOARD DESIGN
Created an intuitive interface for farmers to visualize predictions and alerts
TESTING & FEEDBACK
Deployed early version locally and iterated based on farmer feedback
PRESENTATION & INCUBATION
Secured 2nd place in the hackathon and a free incubation slot at i3p
This platform shows great potential in helping farmers make informed decisions. The predictive insights are actionable, and the team’s execution was excellent under hackathon constraints.
TECHNOLOGIES & SKILLS
