Data Services

Menu of Services

IoT Sensing Technology and Data Acquisition Systems

We provide IoT remote and proximal sensing technology for soil mapping, topographic analysis, and estimating biomass, ground cover, crop residue, and nitrogen content. Our data acquisition systems include technology such as PlantMap3D, which uses stereo imaging cameras and custom software to collect data.
Protocol and Data Management Plan

We can provide protocol review, customization of hardware and software, data forensics and quality management services, and researcher-level early data entry.
Data Engineering

We can provide custom data visualizations, automated data pipelines, data analysis, and custom web dashboards.

Clients

GROW

GROW (Getting Rid Of Weeds) is a scientist-led network coordinating research to help farmers across the U.S. fight herbicide resistance with a greater diversity of weed-control strategies to complement chemical use. GROW provides information on tools such as cover cropping, increased crop competition, harvest weed seed control, and more. The Precision Sustainable Agriculture Data Services team has created an automated pipeline using the Google Drive API to manage, distribute, and collect custom datasheets for a GROW research team studying the efficacy of harvest weed seed control.

PSA — On-Farm Tech Dashboard

A group of farmers doing an on-farm trial, called PSA On-Farm, hired our Data Services team to develop Tech Dashboard to allow them to review data in real time. It’s a custom, site-by-site, tailored, secure dashboard built in React and backed by a node.js API on a PostgreSQL database with OAuth2 authentication. It has since become a free, open-source tool that Precision Sustainable Agriculture offers to farmers and researchers across the country. Tech Dashboard users can enroll their farm sites, edit their data, visualize data transmissions, and submit issues to a bug tracker for review by the Data Services team. To drive data ingestion into the Tech Dashboard, our team used KoboToolbox, an Open Data Kit-compliant forms system. We built a custom Python parser to transform submissions into normalized data with a modular dictionary-based schema constructor.

CRAFT — Data Management

For Citrus Research and Field Trial (CRAFT) in Florida, Texas, and California, the Precision Sustainable Agriculture (PSA) Data Services team is doing form data ingestion on grower management of citrus huanglongbing (HLB) disease, also known as citrus greening disease. USDA has contracted PSA to serve the HLB Multi-Agency Coordination (MAC) Group with partners such as APHIS and the Citrus Boards of FL, TX, and CA to ingest form data for citrus growers and researchers, build public and internal dashboards to visualize their HLB management strategies, and ultimately provide data access to external researchers for analysis.

Forage Sensor Box

In collaboration with Holland Scientific, Precision Sustainable Agriculture turned a commercial sensor prototype into a plant mapping device: the Forage Sensor Box. This low-cost tool uses four sensors (time-of-flight laser, ultrasonic canopy height, and two optical bands, 670 and 780 nm) to characterize the crop canopy. We used the Forage Sensor Box with ground-truthed biomass for multiple cover crop species to map biomass estimations up to 4,000 kg per hectare. We created a Databricks-style automated pipeline using R and Python to ingest biomass scans, inspect and clean data, and store it in Azure Blob Services. Our models improved estimated saturation levels for grasses and legumes substantially over prior satellite imagery work. Farmers and researchers can mount the Forage Sensor Box on a tractor to collect data while working in fields or attach it to a monopod to carry through fields. For future projects, we plan to implement a web dashboard to generate crop performance maps with ongoing updates to calibration models.

DRIVES — Diverse Rotations Improve Valuable Ecosystem Services

Our team is providing the DRIVES project with website design and data hosting for a network of agroecologists collecting long-term crop rotation experiment data. This project consists of regularizing legacy data from a number of experiments across North America and making them available for researchers to upload, review, edit, and access for analysis. Data are stored in a PostgreSQL database and served through a Directus instance that provides both a browser interface and an API layer.