STEAM Impact Project

The Design Challenge

While interest in foraging and sustainable living has surged, beginner foragers face a steep, intimidating learning curve. There is a critical need for an app that provides reliable, real-time identification assistance, ecological context, and community-verified safety alerts. The goal of this project was to design an application that replaces fear with education, ensuring users can confidently explore nature without compromising their safety.

Summary of Research

To transform the concept of Forage Finder into a viable, safe, and highly functional digital product, extensive research was conducted across four primary domains: UX/UI design, mycology, technical integrations, and risk management.

1. User Interface (UI/UX) Research

  • Contextual Inquiry: Researching how foragers interact with technology in the wild. Foragers often have muddy hands, are dealing with bright sunlight (glare), or are balancing a basket. This dictated a need for high-contrast UI, large touch targets, and a seamless one-handed navigation flow.

2. Mycological & Ecological Research

  • Sprouting Mechanics (The Rain Factor): Deep-diving into the lifecycle of fungi to understand when mushrooms actually appear. Research shows different species react uniquely to weather; some pop up 24–48 hours after a heavy rain, while others require days of sustained humidity. Mapping these patterns was essential for building predictive features

3. Weather API & Technical Integration Research

  • Data Sourcing: Evaluating third-party weather APIs (like OpenWeatherMap or Apple WeatherKit) to determine which services offer the most accurate historical rainfall data, humidity levels, and soil temperature tracking.

  • Algorithm Integration: Researching how to cross-reference that live weather data with our mushroom database. The goal was to translate raw meteorological data (e.g., “2 inches of rain in the last 48 hours”) into actionable user insights.

  • AI Capabilities & Limitations: Investigating how image-recognition models can handle natural variables. Mushrooms change appearance drastically based on age, insect damage, or rot.

  • The “Look-Alike” Problem: Researching how to train an AI to not just identify a mushroom, but specifically flag minute differences between a choice edible and its toxic “deadly look-alike” (e.g., telling a Meadow Mushroom apart from a Destroying Angel).

NEXT STEPS – Risk Mitigation & Legal Safety Research

  • Designing for Liability: Consulting legal frameworks regarding crowd-sourced and AI-generated information. Because a mistake can be fatal, research was required on how to draft ironclad disclaimers and community guidelines.

  • Fail-Safe UX Features: Researching behavioral psychology to understand how to prevent over-confidence in users. This led to a design that never gives a 100% definitive answer via AI. Instead, the UI enforces a multi-step verification checklist and explicitly prompts users with a “When in doubt, throw it out” warning before they log a find.

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Link to App

Mushroom Foraging App https://foragefinder-v7.netlify.app/