The Pest Control Living Database (PCLD) is a USDA-funded initiative (project #1023888) that integrates agricultural pest observation data, biological traits of insects, and satellite-based Earth observation resources into a comprehensive, interactive data resource. Designed to streamline data-driven analysis for agricultural pest management, PCLD enables scientists, researchers, and growers to leverage data science and remote sensing technologies to predict pest dynamics, ultimately guiding agricultural decision-making and stewardship worldwide.
The Pest Control Living Database provides:
- Over 100,000 pest-related observations capturing insect activity, abundance, and impacts on crop yields.
- Integrated remote sensing datasets tailored for agricultural sampling locations.
- Detailed insect traits data to facilitate ecological and agricultural research.
- Visualizations of remote sensing datasets and agricultural data.
- Standardized templates for organizing and contributing data.
Explore the detailed slideshow for the PCLD here.
├── app
│ ├── dataset_defns # Definitions for datasets used by the database
│ ├── live_database # Primary database files and configuration
│ ├── secrets # Sensitive configuration and authentication details
│ ├── templates # HTML templates for web interface
│ └── pycache # Python compiled files cache
├── data # Miscellaneous data for database initialization or reference
├── gee_apps # JavaScript apps deployed on Google Earth Engine
└── llm_trait_pipeline # Standalone LLM pipeline for automated pest trait discovery
docker-compose.yml: Main Docker configuration, orchestrating all core services.
We invite contributions of datasets containing information on pest abundance, natural enemies, parasitism/predation rates, or pest-related crop damage. Ideal datasets include over 100 farm-years of observations. Minimum data requirements are:
- Crop sampled
- Sampling date
- Metric type (e.g., pest abundance, predation rate)
- Measurement (per sampling unit)
- Management unit or unique farm ID
- Insect identification (if known)
- Geographical coordinates (if shareable)
- Sampling methodology (metadata)
Submit completed datasets using provided templates to Richard Sharp.
This project is managed by an interdisciplinary team including:
- Becky Chaplin-Kramer (rchaplin@umn.edu)
- Colleen Miller (Colleen Miller)
- Danny Karp (dkarp@ucdavis.edu)
- Richard Sharp (rich@springinnovate.org)
For general inquiries or further details, please contact the project leads above.