GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Operational Water Supply Forecasting for Western US Rivers
Agency: Department of Agriculture - Farm Production and Conservation
Problem addressed: Improved predictions of seasonal water supply forecasts to support irrigated agriculture planning.
Purpose and benefits: The National Water and Climate Center has a multi-model machine-learning metasystem (M4) for generating water supply forecasts. This model uses AI and other data-science technologies to reduce forecast errors, helping stakeholders make better decisions about water supply availability.
Topic area: Service Delivery
Development stage: Deployed
First used: 2024-01-01
US federal AI use case: Standardization of Cut Flower Business Names for Message Set Data
Agency: Department of Agriculture - Marketing and Regulatory Programs
Development stage: Retired
US federal AI use case: Intelligent Ticket Routing
Agency: Department of Agriculture - Office of the Chief Information Officer
Development stage: Retired
US federal AI use case: Predictive Maintenance Impacts
Agency: Department of Agriculture - Office of the Chief Information Officer
Development stage: Retired
US federal AI use case: Approximate String Matching (aka fuzzy matching) to Standardize Data
Agency: Department of Agriculture - Marketing and Regulatory Programs
Problem addressed: Entities (producers, commodities, facilities, etc) use different names in our data systems, making accurate analysis by entity impossible without matching and resolving them.
Purpose and benefits: Benefits include reduced labor hours compared to manual data cleaning, makes near-real-time reporting possible, and increases data accuracy which enables program managers to conduct efficient policy enforcement and program monitoring.
Topic area: Service Delivery
Development stage: Deployed
First used: 2023-05-08
US federal AI use case: Automated PDF Document Processing and Information Extraction
Agency: Department of Agriculture - Marketing and Regulatory Programs
Problem addressed: Plant Protection and Quarantine (PPQ) has important program and workforce related information stored in thousands of PDFs, which cannot easily be used for analytics.
Purpose and benefits: Benefits include making information accessible and timely, increasing data-driven decision making, and saving large amounts of time compared to previous manual methods used.
Topic area: Admin Functions
Development stage: Deployed
First used: 2023-01-16
US federal AI use case: Census of Agriculture Propensity Scores via Machne Learning
Agency: Department of Agriculture - Research, Education and Economics
Problem addressed: The Census of Agriculture is collected every 5 years and requires high participation levels from individuals and agricultural operations to provide relevant agricultural data to the nation.
Purpose and benefits: This model predicts how likely individuals or operations are to complete the Census of Agriculture. The predictions can help data collectors decide where they need to focus their efforts in order to get more complete census responses.
Topic area: Service Delivery
Development stage: Deployed
First used: 2022-10-01
US federal AI use case: Conservation Effects Assessment Project
Agency: Department of Agriculture - Farm Production and Conservation
Problem addressed: The purpose of the use case is to predict the conservation effects of cropland practices in real time, with no technical skill required.
Purpose and benefits: Such models would allow field conservation planners to have real-time conservation effects on sediment and nutrients.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Digital Imagery (no-change) for NRI Program
Agency: Department of Agriculture - Farm Production and Conservation
Problem addressed: AI algorithms are used to look at landscape images and detect if the landscape has changed from year to year.
Purpose and benefits: About 72,000 aerial images are interpreted by dozens of technicians each year to collect data for the National Resources Inventory (NRI) program. This case would decrease the number of labor hours required of technicians to manually interpret images.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Nutrition Education & Local Access Dashboard
Agency: Department of Agriculture - Food, Nutrition, and Consumer Services
Problem addressed: The goal of this dashboard is to provide county-level information on nutrition education and local food access, alongside other metrics related to hunger and nutritional health. This interactive dashboard can provide specific details based on the properties of farm to school intensity and size, program activity intensity, demographics, fresh food access, school size, and program participation. These properties allow users to find similar states based on any of these characteristics, opening up opportunities for partnerships with states they may not have considered.
Purpose and benefits: Benefits include consolidating related data in one location for informing nutrition programming, and identify potential partners for nutrition programs based on similarities.
Topic area: Benefits Processing
Development stage: Deployed
First used: 2023-06-23
US federal AI use case: Survey Text Remarks Value Scoring
Agency: Department of Agriculture - Research, Education and Economics
Development stage: Retired
US federal AI use case: Survey Outlier Detection Model
Agency: Department of Agriculture - Research, Education and Economics
Problem addressed: Anomalous or inaccurate values in surveys must be correctly identified and addressed to ensure high data quality.
Purpose and benefits: The use of this model reduces manual labor and improves data quality.
Topic area: Other
Development stage: Deployed
First used: 2022-05-27
US federal AI use case: Multilingual Translation of Recalls and Public Health Alerts
Agency: Department of Agriculture - Food Safety
Development stage: Retired
US federal AI use case: Genomic Analyses of Pathogen Subtypes
Agency: Department of Agriculture - Food Safety
Problem addressed: The purpose of this use case is to use machine learning (ML) methods to group foodborne bacteria based on patterns in their genes, then connect this information with available health data to evaluate foodborne illness risk to public health.
Purpose and benefits: Expected benefits include improving our understanding of foodborne illness, identifying key genes and trends, and evaluating those that are important for public health.
Topic area: Science
Development stage: Pilot
First used: 2024-10-01
US federal AI use case: Foodborne Illness Source Attribution
Agency: Department of Agriculture - Food Safety
Problem addressed: The Interagency Food Safety Analytics Collaboration (IFSAC) - a partnership between the Centers for Disease Control and Prevention (CDC), the U.S. Food and Drug Administration (FDA), and the Food Safety and Inspection Service (FSIS) - has used computer-based methods to predict the likely sources of foodborne illnesses in humans.
Purpose and benefits: Expected benefits include improving our understanding of what foods contribute the most to illnesses, which can help in creating measures and policies to reduce illnesses and improve food safety.
Topic area: Science
Development stage: Pilot
First used: 2024-01-29
US federal AI use case: Rangeland Analysis Platform
Agency: Department of Agriculture - Research, Education and Economics; Farm Production and Conservation
Problem addressed: Provides ranchers and land managers with information to detect and manage invasive species spread, wildfire risks, woody species encroachment, wildlife habitat condition, and wind erosion risks with high precision across the western U.S.
Purpose and benefits: The Rangeland Analysis Platform (RAP) allows users to track changes in plant growth and coverage over time. By monitoring the condition of agricultural ecosystems and the impact of conservation efforts, it can guide conservation practices, invasive species management, drought and wildfire responses, and wildlife habitats.
Topic area: Energy and Environment
Development stage: Deployed
First used: 2017-01-01
US federal AI use case: Predictive Cropland Data Layer
Agency: Department of Agriculture - Research, Education and Economics
Problem addressed: The purpose of this system is to predict crop rotations.
Purpose and benefits: Benefits include improving data quality of area-based surveys and providing assistance to field office personnel.
Topic area: Admin Functions
Development stage: Deployed
First used: 2021-02-15
US federal AI use case: Dam Inspection Report Document Processing
Agency: Department of Agriculture - Farm Production and Conservation
Problem addressed: The AI use case allows our staff to identify the biggest issues and trends across 2,100+ dams.
Purpose and benefits: The benefit and positive outcome from the AI is a wholistic understanding of dam issues across 2,100+ dams. This allows us to better analyze the data and build a risk-prioritization system to rank sites and projects to work on, reducing labor hours required to complete the task manually, and reducing the dependence on specific staff to maintain knowledge of each dam's inspection response.
Topic area: Admin Functions
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: The AI is used for information extraction that informs dam portfolio decisions. However, the principal basis for decisions is made by humans for the repairs, and is vetted through aerial imagery analysis, the Oklahoma Conservation Commission, and associated dam sponsors.
First used: 2023-12-20
US federal AI use case: Portfolio Approval and Management (PAM) Bot
Agency: Department of Agriculture - Research, Education and Economics
Problem addressed: The purpose of the model is to improve the Economic Research Service (ERS) research approval process.
Purpose and benefits: The system reduces the time it takes to fill out information and seek approval, improves information accuracy, and brings visibility to the approval status across various division functions.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: GIS Invasive Tree Extraction for Field Level Users
Agency: Department of Agriculture - Farm Production and Conservation
Problem addressed: The tool automatically identifies invasive Eastern Red Cedar trees from the surrounding landscape. This eliminates manual processing by planners.
Purpose and benefits: Automated tool removes tedious maual process and returns planners' time to work on higher-value priorities. This saves time and money and provides better customer service.
Topic area: Energy and Environment
Development stage: Deployed
First used: 2019-07-15
US federal AI use case: DISTRIB-II: Habitat Suitability of Eastern United States Tree
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: The goal of this use case is to provide information about current and future habitat suitability of east United States tree species. Machine learning is used to develop models that can predict relative abundance of individual tree species in the future.
Purpose and benefits: These models provide forest land managers with information about how habitats could change under various future scenarios which can assist in resource planning. The information provided by the models can also result in cost saving and resource effectiveness by reducing costs to manage unsuitable tree species and instead suggesting species that could be more suitable.
Topic area: Science
Development stage: Deployed
First used: 2019-09-12
US federal AI use case: FSA FLP Chatbot
Agency: Department of Agriculture - Farm Production and Conservation
Problem addressed: This AI use case is researching program handbooks and generating contextually and factually accurate responses from highly technical Farm Loan Program information.
Purpose and benefits: Empowers employees to provide better service and cost savings via faster response times to program-specific questions. Increases accuracy of content and knowledge in program delivery.
Topic area: Service Delivery
Development stage: Pilot
First used: 2025-09-15
US federal AI use case: ROE Document Recognition - RoeDR
Agency: Department of Agriculture - Farm Production and Conservation
Problem addressed: The purpose of this model is to automate a slow manual process by analyzing documents from producers and Authorized Insurance Providers (AIPs), picking the appropriate page from the documents, reading the signature date and producer signature name, converting the date and name to text, and loading it into an application.
Purpose and benefits: Saves employee time processing written agreements and having to input the data manually.  We can then use the data for reporting purposes.
Topic area: Benefits Processing
Development stage: Deployed
First used: 2024-05-15
US federal AI use case: IRIS
Agency: Department of Agriculture - Marketing and Regulatory Programs
Development stage: Retired
US federal AI use case: Ticket Resolution Categorization (Incident/Change)
Agency: Department of Agriculture - Office of the Chief Information Officer
Development stage: Retired
US federal AI use case: Ticket Templatization
Agency: Department of Agriculture - Office of the Chief Information Officer
Development stage: Retired
US federal AI use case: File Rename
Agency: Department of Agriculture - Farm Production and Conservation
Problem addressed: This tool extracts specific text from a PDF and renames the file to comply with electronic standard naming conventions
Purpose and benefits: Cost and time savings in regard to file management and data governance
Topic area: Admin Functions
Development stage: Pilot
First used: 2025-08-11
US federal AI use case: Rapid Drafting of ARS Research Summaries
Agency: Department of Agriculture - Research, Education and Economics
Problem addressed: The purpose of this tool is to summarize ongoing research from internal Agricultural Research Service (ARS) documents to allow program staff to efficiently understand ARS research outputs and create accurate and timely summary documents, such as briefing papers, talking points for leadership, and speeches.
Purpose and benefits: This system will give staff more time for higher-priority duties, and leadership will be able to more confidently answer questions, justify budget requests, and ensure that our research is innovative and relevant.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: DS Hub Geo-metadata generation
Agency: Department of Agriculture - Farm Production and Conservation
Problem addressed: The purpose of this AI use case is to generate metadata for Natural Resources Conservation Service (NRCS) datasets, ensuring consistency, accessibility, and compliance through generative AI.
Purpose and benefits: Expected benefits include increased data accessibility, reduced manual workload, minimized errors, better dataset understanding, and fast data retrieval for stakeholders.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Dynamic Soils Hub
Agency: Department of Agriculture - Farm Production and Conservation
Problem addressed: The Dynamic Soils Hub (DS Hub) under the Natural Resources Conservation Service (NRCS) is a tool designed to help both government workers and the public understand and analyze soil information.
Purpose and benefits: The DS Hub links different soil and conservation databases, making it easier to evaluate the environmental benefits of conservation programs. This enhances the USDA’s ability to study and report on how soil properties change with conservation efforts over time.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Cover Crop Mapping
Agency: Department of Agriculture - Farm Production and Conservation
Problem addressed: The agency needs to independently check and track the use of cover crops on farms. Yearly maps are made using satellite images and models of plant growth to measure the use of cover crops on farms in the U.S. Midwest.
Purpose and benefits: Benefits by helping the agency independently check and track how many farmers are using cover crops, reducing the need for on-site visits to determine if cover crops are present on a field.
Topic area: Service Delivery
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Planting Date Detection
Agency: Department of Agriculture - Farm Production and Conservation
Problem addressed: The agency needs to independently check and confirm the planting dates reported by farmers to ensure the integrity of their programs. Maps containing planting dates are made using satellite images and models of plant growth, which help to find out the planting dates for corn, soybean, and winter wheat on farms in the U.S. Midwest.
Purpose and benefits: This data helps the agency independently check and confirm the planting dates reported by farmers to ensure the integrity of their programs, reducing the need for on-site visits to determine when a field was planted.
Topic area: Service Delivery
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Acreage and Crop Type Validation
Agency: Department of Agriculture - Farm Production and Conservation
Problem addressed: Making sure crop programs are fair and correct, this tool helps the agency validate that the reported field sizes and crop types are accurate.
Purpose and benefits: This data helps the agency independently verify the accuracy of reported field sizes and crop types, supporting efforts to ensure the integrity of crop programs.
Topic area: Service Delivery
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: US Poultry and Operations Dataset
Agency: Department of Agriculture - Marketing and Regulatory Programs
Problem addressed: Identifies the location and distribution of poultry agricultural facilities in the United States.
Purpose and benefits: Developing this dataset/model provides detailed data on poultry farm locations and populations, which is essential for planning animal health emergencies and predicting the spread of diseases.
Topic area: Emergency Mgmt
Development stage: Deployed
First used: 2019-06-03
US federal AI use case: Equine Operations and Populations Dataset for the U.S.
Agency: Department of Agriculture - Marketing and Regulatory Programs
Problem addressed: The purpose of this case is to develop a dataset that addresses the problem of not having complete information about where horse farms are located and how many horses they have.
Purpose and benefits: Developing this dataset/model provides detailed data on horse farm locations and populations, which is essential for emergency preparedness and response and for predicting the spread of equine (horse or members of the horse family) diseases.
Topic area: Emergency Mgmt
Development stage: Pre-deployment
First used: 2024-09-01
US federal AI use case: NASS Automated Editing Tool
Agency: Department of Agriculture - Research, Education and Economics
Problem addressed: The purpose of this model is to determine if an answer on a survey is valid or invalid. If an answer is classified as invalid, a model will then suggest a corrected value.
Purpose and benefits: The main benefit is reducing labor hours on redundant data cleaning tasks. It could possibly increase the accuracy of National Agricultural Statistics Service data.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: County-Level Remotely-Sensed Corn and Soybean Yield Estimation
Agency: Department of Agriculture - Research, Education and Economics
Problem addressed: The purpose of this tool is to estimate yearly corn and soybean yields for each county using satellite images.
Purpose and benefits: Provide county-level crop yield statistics which allow stakeholders to make more informed planning and decisions.
Topic area: Other
Development stage: Deployed
First used: 2007-01-01
US federal AI use case: NASSportal Intranet Agent
Agency: Department of Agriculture - Research, Education and Economics
Problem addressed: Quick identification of internal policies and procedures for staff to execute programs
Purpose and benefits: Increased data quality and program efficiency
Topic area: Admin Functions
Development stage: Pre-deployment
First used: 2025-03-27
US federal AI use case: XyloTron/XyloPhone Wood Identification System
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: The purpose of these tools is to identify different types of wood based on their cross-section. These tools will help industries follow laws and support law enforcement in meeting national (e.g. Lacey Act) and international (e.g. CITES) regulations.
Purpose and benefits: Corrects product claims for wood products and improves US forest products industry health by providing an even playing field for domestic products.
Topic area: Science
Development stage: Pilot
First used: 2016-01-01
US federal AI use case: Incident Invoice Document Understanding
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Staff is overburdened by manual document handling, resulting in less time to focus on high-value strategic tasks. This tool automates data extraction from forms and invoices.
Purpose and benefits: Enhanced workflow efficiency, better knowledge management, improved accuracy and consistency, frees up staff to focus on strategic tasks rather than manual document handling.
Topic area: Emergency Mgmt
Development stage: Pre-deployment
First used: 2025-06-16
US federal AI use case: Forest disease detection and screening
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: To improve tree disease diagnosis and screening.
Purpose and benefits: Facilitate ongoing efforts within and outside the Forest Service to manage diseases of forest trees. Provide tools for point-of-care diagnosis of tree diseases.
Topic area: Science
Development stage: Pre-deployment
First used: 2020-08-03
US federal AI use case: Use of LLMs for data extraction
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: The purpose of this model is to quickly gather information from scientific papers to track the spread of plant diseases.
Purpose and benefits: This method would save time compared to manually collecting the information, which can be slow and error-prone.
Topic area: Science
Development stage: Pre-deployment
First used: 2023-10-01
US federal AI use case: IPWG Application Survey Analysis
Agency: Department of Agriculture - Natural Resources and Environment
Development stage: Retired
US federal AI use case: Fire Resilient Landscapes
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Assists in planning treatments to reduce wildfire risk and calculate treatment costs
Purpose and benefits: Cost savings and improving efficiencies
Topic area: Science
Development stage: Pilot
First used: 2021-08-01
US federal AI use case: PC Rasterize
Agency: Department of Agriculture - Natural Resources and Environment
Development stage: Retired
US federal AI use case: Spread and Balance Sample Design
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: The purpose of this tool is to produce samples that are well spread and balanced, reducing sampling cost and error.
Purpose and benefits: Providing better information about our natural resources for less spend
Topic area: Science
Development stage: Pilot
First used: 2024-06-01
US federal AI use case: Regression, Classification, Clustering with Hilbert Curves
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Implementation of many machine learning techniques into applied research
Purpose and benefits: Better predictions in applied research that cost less to implement.
Topic area: Science
Development stage: Pilot
First used: 2024-06-01
US federal AI use case: The Big Data, Mapping, and Analytics Platform (BIGMAP) Project
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: To produce maps of various characteristics of US forests at all locations, based on field plots measured by the US Forest Service Forest Inventory and Analysis (FIA) program only at some locations.
Purpose and benefits: BIGMAP maps can be used for visualizations, modeling, and decision-making at regional and sub-state scales. Provides greater precision in estimates and leads to more informed decisions about the forest resources in the US.
Topic area: Science
Development stage: Deployed
First used: 2019-01-01
US federal AI use case: BirdNET to detect bird vocalizations for research and species monitoring
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: BirdNET quickly scans thousands of hours of forest audio recordings to detect bird calls from species that are important for forest monitoring, like American goshawks, black-backed woodpeckers, spotted owls, and willow flycatchers.
Purpose and benefits: This decreases the time and cost associated with manually listening to recordings to identify bird calls.
Topic area: Energy and Environment
Development stage: Deployed
First used: 2021-06-01
US federal AI use case: Predictive flood modeling
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: The model is intended to solve the overtopping and/or failure of at-risk road culverts (drains) due to projected future extreme weather induced flooding, by identifying potential undersized culverts prior to field visit.
Purpose and benefits: Reducing risks to forest access following future flooding and fire. Reducing costs associated with road management and repair besides the restoration costs of the drains themselves.
Topic area: Transportation
Development stage: Deployed
First used: 2025-10-01
US federal AI use case: FuelCast
Agency: Department of Agriculture - Natural Resources and Environment
Development stage: Retired
US federal AI use case: R1 Forest Vegetation Modeling
Agency: Department of Agriculture - Natural Resources and Environment
Development stage: Retired
US federal AI use case: ESRI Support Chat Bot
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: The purpose of this chatbot is to help handle requests for support with geospatial software between our team and the software vendor.
Purpose and benefits: The benefits include saving time when dealing with Environmental Systems Research Institute (ESRI) support issues and reducing the number of specific ESRI support tickets that need to be sent to the Forest Service Geospatial Helpdesk or to ESRI through contract support services.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Wildlife Deterrent System
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: The purpose of the AI device is to keep coyotes out of a fenced area by blocking their entry through a gap in the fence, while still allowing other wildlife to pass through.
Purpose and benefits: The benefits include making ecological research possible that couldn't be done otherwise, and saving time by reducing the need to watch camera footage and manually control the fence.
Topic area: Science
Development stage: Pilot
First used: 2025-04-21
US federal AI use case: The Lost Meadows Model
Agency: Department of Agriculture - Natural Resources and Environment
Development stage: Retired
US federal AI use case: Markov random fields for mixed forests
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: A level of uncertainty can exist in model predictions and sometimes that level of uncertainty is incorrectly assessed, leading to less effective decision-making when based on the model predictions.
Purpose and benefits: These methods to correctly evaluate uncertainty in model predictions help stakeholders make more informed and effective decisions.
Topic area: Science
Development stage: Pre-deployment
First used: 2022-10-10
US federal AI use case: AI for regional forest mapping and monitoring
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: The purpose of this model is to use existing satellite images and forest survey data from the USDA Forest Service to create detailed maps of forest structures. Continuing research to understand and communicate errors and uncertainties in mapping and to improve algorithms over time.
Purpose and benefits: In addition to basic and applied research innovations, the application provides maps of forest structure (e.g., tree density and size, timber volume) and composition (tree species) which supports National Environmental Policy Act (NEPA) and other planning operations. These data may provide cost savings by either reducing the need to generate independent data through geographic information systems or field work. This research will help land managers be more effective and efficient with their planning.
Topic area: Energy and Environment
Development stage: Deployed
First used: 2002-10-01
US federal AI use case: IOL Focus Group and Survey Sensemaking
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Processing and interpretation of questionnaires and transcripts can be slow and tedious. Using Google NotebookLM can improve the process.
Purpose and benefits: Cost savings, time savings, mission execution, and reduced timelines for large-scale projects.
Topic area: Science
Development stage: Pre-deployment
First used: 2025-03-31
US federal AI use case: Esri ArcGIS Pro Deep Learning Modules
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Object detection from aerial images
Purpose and benefits: The purpose of this tool is to enhance scientific modeling and analysis, which will standardize Geographic Information System (GIS) workflows for modeling and analytics.
Topic area: Science
Development stage: Deployed
First used: 2024-04-01
US federal AI use case: QUIC-Fire processing and analysis
Agency: Department of Agriculture - Natural Resources and Environment
Development stage: Retired
US federal AI use case: Analysis of prescribed fire turbulence data
Agency: Department of Agriculture - Natural Resources and Environment
Development stage: Retired
US federal AI use case: Zen AI Image Analysis
Agency: Department of Agriculture - Marketing and Regulatory Programs
Problem addressed: Automated AI image analysis tool for screening and detection methods to increase efficiency and reduce human error in sterile insect technique programs.
Purpose and benefits: The image analysis tool will improve efficiency and reduce costs in the USDA Animal and Plant Health Inspection Service (APHIS) Mexican Fruit Fly backup colony and the Mexican Fruit Fly Mass Rearing Facility by enabling previously manual processes to become automated, less prone to subjective handler error, and transferable to Field Operations staff without advanced training.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Conservation Management Benefits Navigator (CMBN)
Agency: Department of Agriculture - Farm Production and Conservation
Problem addressed: Updating technical policy currently involves lengthy literature review that can take many months to complete. National Resources Conservation Service (NRCS) struggles to update technical policy and make new technologies available to producers within a 5-year cycle. NRCS is often forced to use contracts for literature review completion due to lack of staff possessing expertise needed for completing the reviews.
Purpose and benefits: Reduces the amount of time to update National Resources Conservation Service (NRCS) conservation practice standards from years to months; makes new technology available to U.S. producers more rapidly than current possible timeframes.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: ProcureSight
Agency: Department of Agriculture - Departmental Administration
Problem addressed: The primary use is to assist with market research and responsibility determination.
Purpose and benefits: This is a free solution and it use will increase procurement professional productivity as ProcureSight transforms public datasets from SAM.gov and USASpending, enabling users to perform highly precise searches against this public procurement data. This is cost saving and improving government efficency.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
First used: 2024-10-14
US federal AI use case: Axon+ Copilot Studio Chatbot
Agency: Department of Agriculture - Research, Education and Economics
Problem addressed: To cut down the time staff spend searching our intranet for information to administer programs & projects and deliver answers that they would not be able to find otherwise.
Purpose and benefits: Time savings for employees searching for internal information.
Topic area: IT
Development stage: Pilot
First used: 2025-05-30
US federal AI use case: Plum Island handwriting and text extraction from scanned pdfs
Agency: Department of Agriculture - Marketing and Regulatory Programs
Problem addressed: To reduce manual intervention and automate searchability of Plum Island lab manuals and scientific documents.
Purpose and benefits: Time savings that could translate to more personnel cycles on scientific needs as opposed to manual labor searches.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: NWRC ND Field Station Birds and Agriculture Project
Agency: Department of Agriculture - Marketing and Regulatory Programs
Problem addressed: Use machine learning to detect, classify, and count birds damaging crops. Systems allowed to selectively deploy tools to disrupt bird activity.
Purpose and benefits: Potential cost savings include automation of deterring bird damage to agricultural crops.
Topic area: Science
Development stage: Pilot
First used: 2025-03-01
US federal AI use case: NWRC LSS Bioinformatic Coding
Agency: Department of Agriculture - Marketing and Regulatory Programs
Problem addressed: Troubleshoot code used in analysis
Purpose and benefits: Time savings for employees is the main expected benefit
Topic area: Science
Development stage: Pilot
First used: 2025-01-01
US federal AI use case: NWRC Rabies ML A.D.
Agency: Department of Agriculture - Marketing and Regulatory Programs
Problem addressed: Speeding up the processing and identification of species within trail camera images. Improving the ability to answer management questions.
Purpose and benefits: Time savings for employees is the main expected benefit
Topic area: Science
Development stage: Pilot
First used: 2025-03-03
US federal AI use case: NWRC Rabies ML M.H.
Agency: Department of Agriculture - Marketing and Regulatory Programs
Problem addressed: Speeding up the processing and analysis of large genomic datasets to track diseased animals, which helps inform managers the best course of action for emergency response and intervention to limit virus spread.
Purpose and benefits: Time savings for employees is the main expected benefit
Topic area: Science
Development stage: Pilot
First used: 2025-03-03
US federal AI use case: NWRC Pepin ML & AI
Agency: Department of Agriculture - Marketing and Regulatory Programs
Problem addressed: Analyze simulation data from computational models; staff burden of processing screen trail camera photos
Purpose and benefits: Decrease data processing time, save staff time
Topic area: Science
Development stage: Pilot
First used: 2025-01-01
US federal AI use case: NWRC Genetics Metadata
Agency: Department of Agriculture - Marketing and Regulatory Programs
Problem addressed: Assist with data processing, convert data to standard format for publication, infer origin populations
Purpose and benefits: Time savings for employees is the main expected benefit
Topic area: Science
Development stage: Pilot
First used: 2025-01-01
US federal AI use case: ARGUS AI Supply Chain Interference
Agency: Department of Agriculture - Office Of Homeland Security
Problem addressed: Identify potential risks in the supply chain.
Purpose and benefits: As a specialty-focused information resource, ARGUS AI Supply Chain Influence’s core strength is its ability to collect, organize, and summarize open-source information relating to suppliers.
Topic area: International Affairs
Development stage: Deployed
First used: 2022-09-15
US federal AI use case: GovChat
Agency: Department of Agriculture - Research, Education and Economics
Problem addressed: AI assistant that helps with time consuming tasks such as getting answers to general inquiries using natural language
Purpose and benefits: A general chatbot without a proprietary interface that can accelerate completion of mundane tasks by employees
Topic area: Other
Development stage: Pilot
First used: 2024-10-01
US federal AI use case: HR Chatbot
Agency: Department of Agriculture - Food Safety
Problem addressed: The HR chatbot provides support to Human Resources personnel by addressing benefits-related inquiries, particularly during the open season period.
Purpose and benefits: Reduced call volume and wait times, increased efficiency and productivity, scalable self-service model, continuous learning and feedback integration
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: AR-Enabled Mobile App for Policy and Regulation Review
Agency: Department of Agriculture - Food Safety
Problem addressed: Enables inspectors to perform remote dispositions and receive training in real-time, hands-free.
Purpose and benefits: Reduced travel time and costs, especially in hard-to-staff or remote locations. Expanded inspection coverage through flexible deployment of remote veterinary oversight. Improved job satisfaction and retention by minimizing travel burdens and offering modern tools. Enhanced training and support via real-time remote guidance from experienced veterinarians.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: LLM-Powered Drafting Assistant for NRs and MOIs
Agency: Department of Agriculture - Food Safety
Problem addressed: Supports inspectors in drafting Noncompliance Records (NRs) and Memorandums of Interview (MOIs).
Purpose and benefits: Streamlined noncompliance and memoranda of interview (MOI) documentation. Improved data reuse, access, and interoperability. Improved consistency and reduced time requirements.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Chatbot for Meat and Poultry Hotline & OPACE Efficiency
Agency: Department of Agriculture - Food Safety
Problem addressed: Live agents are overburdened trying to respond to a high volume of common food safety questions. Opportunity to streamline Office of Public Affairs and Consumer Education operations, freeing staff for higher-value tasks.
Purpose and benefits: Reduces workload on live agents by handling common food safety questions automatically. Improves public access to accurate, real-time food safety information 24/7. Delivers cost savings through automation while maintaining service quality. Ensures privacy and compliance by operating without collecting personal data.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: AI-Enhanced Qlik Reporting for FSIS Data
Agency: Department of Agriculture - Food Safety
Problem addressed: Need deeper insights from Food Safety Inspection Service (FSIS) datasets by identifying trends, anomalies, and predictive indicators that inform decision-making.
Purpose and benefits: AI-enhanced insights for Food Safety Inspection Service data. Faster and more informed decision-making. Supports AI inventory and budget planning.  Governance and tool rationalization.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Consumer Mobile App for Identifying Recalled Products
Agency: Department of Agriculture - Food Safety
Problem addressed: Consumers have a need to verify product safety quickly and efficiently, opportunity to enhance public trust and food safety awareness.
Purpose and benefits: Empowers consumers with real-time recall alerts. Barcode/QR code scanning for instant product verification. AI-driven personalization and predictive alerts.  Supports regulatory compliance and public education. Data feedback loop for continuous improvement.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: AI Camera Technology to identify Product Safety Concerns
Agency: Department of Agriculture - Food Safety
Problem addressed: Business operations with camera technology to detect food safety issues in poultry slaughter lines.
Purpose and benefits: Enhanced detection accuracy and consistency. Operational efficiency and line speed optimization. Increased scientific rigor and regulatory alignment. Improved data capture and traceability.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Automating ATO/POAM documentation
Agency: Department of Agriculture - Food Safety
Problem addressed: Need for enhanced cybersecurity visibility, response time, and documentation efficiency.
Purpose and benefits: Improved cybersecurity visibility. Faster response time to threats. Greater documentation efficiency. Stronger governance and compliance.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Incident Detection and Response Acceleration
Agency: Department of Agriculture - Food Safety
Problem addressed: Need for improved incident detection and response acceleration
Purpose and benefits: Faster threat containment and mitigation. Continuous monitoring and real-time detection.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Vegetation Specifications Suite (VegSpec)
Agency: Department of Agriculture - Farm Production and Conservation
Problem addressed: Consistent technical expertise is lacking in the development of conservation practices for producers that need to implement emergency and disaster assistance conservation programs, as well as other voluntary conservation programs
Purpose and benefits: Empowers producers to make informed decisions on plant materials used in their operations for conservation, disaster recovery, or production purposes. Consistent and tailored specifications with cost estimations.
Topic area: Service Delivery
Development stage: Pilot
First used: 2024-12-31
US federal AI use case: NETS automated waterway design
Agency: Department of Agriculture - Farm Production and Conservation
Problem addressed: Surveying for and creating engineering designs for relatively simple practices is time consuming and limits getting programs implemented for producers in a timely manner
Purpose and benefits: Reduces producer wait times for practice designs, reduces time investment in simple engineering survey and designs, efficiently uses taxpayer resources.
Topic area: Transportation
Development stage: Pilot
First used: 2025-03-15
US federal AI use case: DamWatch automated leadership reporting
Agency: Department of Agriculture - Farm Production and Conservation
Problem addressed: Providing leadership with information about impacts to dams (high hazard and other dams) after storm events or other natural disaster events is time consuming and can delay leaders receiving needed information for decision making
Purpose and benefits: Improves speed and efficiency of government decision-making to protect life and property in disaster situations
Topic area: Emergency Mgmt
Development stage: Pre-deployment
US federal AI use case: Digital Soil Mapping
Agency: Department of Agriculture - Farm Production and Conservation
Problem addressed: The purpose of this project is to implement new soil map products at local, regional, and national scales to provide more explicit soil information than traditional soil mapping products.
Purpose and benefits: The expected benefits are that AI streamlines the data development process, creates more consistent and detailed maps, and provides objective estimates of uncertainty to support conservation activities, decision makers, and environmental modelers.
Topic area: Science
Development stage: Pilot
First used: 2012-10-01
US federal AI use case: Annual Soil Refresh Data Staging Server Automation
Agency: Department of Agriculture - Farm Production and Conservation
Problem addressed: The process for adding new data to the Staging Server is repetitive and time consuming. We utilize the commercial software UIPath Studio with computer vision to automate this process.
Purpose and benefits: Automates a portion of this process so that it can be done more efficiently and reduce the number of labor hours spent.
Topic area: IT
Development stage: Pilot
First used: 2025-06-02
US federal AI use case: Image generator for presentation support
Agency: Department of Agriculture - Marketing and Regulatory Programs
Problem addressed: There are often scenes of field work that are difficult to photograph for use in educational presentation. AI has been used to generate images that approximate those field operations.
Purpose and benefits: Provides a tool that improves the training of staff and educating agricultural stakeholders.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Forest based and boosted classification and regression methods
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Determining which features of a forest best indicate species presence and amount
Purpose and benefits: Improves the selection of most relevant forest features for making predictions
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: MaxEnt/ENMeval models of habitat suitability
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Predict species habitat under current and future conditions
Purpose and benefits: Improves focus survey efforts, improves conservation efforts, and landscape scale planning
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: ASKTERRA
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Geospatial data and models can be difficult to navigate and understand, especially for non-expert geospatial data users. Large language models give users the ability to ask questions of geospatial data using plain language, and receive useful insights.
Purpose and benefits: This use case would allow anyone to quickly engage with geospatial data in curated ways while taking advantage of asking specific questions within specific areas of interest.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Species Distribution Data analysis
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Machine learning models built for predicting and understanding species distributions and ranges for thousands of different species.
Purpose and benefits: Providing information on where species are distributed improves decision-making for ecosystem management
Topic area: Science
Development stage: Pilot
First used: 2025-01-06
US federal AI use case: A wetland-based habitat suitability model for Spotted Turtles in West Virginia
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Classifying environmental suitability of wetlands for a species of turtle.
Purpose and benefits: The expected benefit of this use case is to create cost savings
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Projected contemporary habitat distribution and quality for wood turtles in the midwestern United States
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Estimating environmental suitability of stream cells for a turtle species.
Purpose and benefits: The expected benefit of this use case is to create cost savings
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Amphibians reveal unexpectedly large differences in potential environmental change responses among ecologically similar habitat specialists
Agency: Department of Agriculture - Natural Resources and Environment
Problem addressed: Estimating environmental suitability for amphibians
Purpose and benefits: The expected benefit of this use case is to create cost savings
Topic area: Science
Development stage: Pre-deployment