PlantAtlas is a free, self-funded project created by Ricky Patel, an engineer with a passion for plants, sustainability, and research.
After experiencing the limitations of the USDA PLANTS Database—slow loading times, limited search capabilities, and cumbersome navigation—I decided to rebuild it from the ground up. Using modern web technologies and SQLite for local storage, I've created a faster, more accessible version that works entirely in your browser. No server roundtrips, no waiting. While the database is particularly useful for researchers, wildlife technicians, taxonomists, and university students, it's been organized to be accessible to anyone interested in plants.
The ".ai" in our domain reflects how AI was used as a development tool to build these applications more efficiently—not because AI is embedded in the user experience. These are straightforward, practical tools: advanced filtering, SQL query capabilities for researchers, and instant CSV/JSON exports. Technology should enhance research, not complicate it.
This project is self-funded and offered completely free. If you find it useful, donations are greatly appreciated to help keep the servers running and development ongoing.
Browse 32,800+ plants with lightning-fast search. Filter by state, native status, family, genus, growth habits, and environmental tolerances. Run custom SQL queries for advanced analysis or use predefined filters. Export results to CSV or JSON instantly. Rebuilt for speed and accessibility.
Access real-time local weather, air quality data, hourly and weekly forecasts, and moon phase information. Discover what to plant based on your USDA Hardiness Zone with detailed growing guides sourced from university extensions. Useful for outdoor planning and growing knowledge.
Built with modern web technologies and SQLite running entirely in your browser. No server delays, no loading spinners. Search 30,000+ plants instantly with local processing power. All data is cached for offline access once loaded.
Find exactly what you need with filters for native/introduced status, drought tolerance, shade requirements, growth habits, and more. Researchers can write custom SQL queries for complex analysis. Export any filtered results with one click to CSV or JSON.