usda-gov/www.usda.gov/sites/default/files/documents/ai_inventory.csv

1	Use Case ID/Name	Mission Area	Agency	Bureau/Component/Office	Topic Area	Intended purpose and expected benefits of use case	Description of AI system's outputs	Current stage of development	Rights-impacting or Safety-impacting	Date Initiated	Development/Acquisition Date	Date Implemented	Date Retired	Open-source link to project code (if available)	Supporting a High Impact Service Provider (HISP)	If yes to supporting a HISP, which one?	Public-facing service in HISP	Developed under contract(s) or in-house	Agency-owned Data Description	Demographic variables used in model features
2	USDA-001: Repair Spend	REE: Research, Education, and Economics	ARS: Agricultural Research Service	Administrative and Financial Management	Mission-Enabling (Internal Agency Support)	The intended purpose of this model is to review financial documents and then classify each expense as money spent on "facility repairs" or "not facility repairs". The expected benefits include reduction of manual hours identifying the types of transactions.	The output of the model is a recommendation of which financial transactions should be identified as "repair" expenses.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	10/1/2019	10/1/2019	6/9/2020			No			Developed with both contracting and in-house resources	Approximately 14,000 financial transactions were used to train the model and finetune its parameters. Approximately 3,500 financial transactions were used to test the performance of the final model.	None;
3	USDA-002: ARS Project Mapping	REE: Research, Education, and Economics	ARS: Agricultural Research Service	Office of National Programs	Science & Space	The intended purpose of this model is to process research plans from various research program portfolios in the Agricultural Research Service (ARS) to find patterns and opportunities between projects. The expected benefits include decreasing the time that humans would spend to manually read, pull out key terms, and group the projects by topic. The model may also find patterns that a human might miss.	The model outputs groups of similar projects and project terms. The output includes metrics (silhouette scores, term rank, importance scores) that show how well the projects and terms in a group match.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	1/1/2020	1/1/2021	5/1/2022			No			Developed with contracting resources	The data is a collection of project plans written by scientists, roughly 600 text documents. The texts are related to publicly available 5-year action plans.	None;
4	USDA-003: NAL Automated Indexing	REE: Research, Education, and Economics	ARS: Agricultural Research Service	National Agricultural Library	Science & Space	This system automatically assigns word tags to agricultural research articles from a controlled list of terms provided by the National Agricultural Library Thesaurus (NALT). The tags can be used to look up and retrieve articles. Using these tags benefits users by making it easier to find the content they are looking for.	The model outputs terms to use as search tags that are specific to the article that the model analyzed.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	6/1/2011	1/1/2012	6/1/2012			No			Developed with both contracting and in-house resources	The data is a collection of text scripts from publishers that have undergone quality assurance and quality control processing.	None;
5	USDA-004: Predictive Modeling of Invasive Pest Species	MRP: Marketing and Regulatory Programs	APHIS: Animal and Plant Health Inspection Service	Plant Protection and Quarantine	Mission-Enabling (Internal Agency Support)	The purpose of the model is to check how likely it is for imported agricultural products from other countries to have pests. Benefits include more reliable discovery and quarantine of invasive pests, preventing pest invasion and making trade safer.	The model outputs are a prediction of whether a product carries an invasive species and what invasive species category the pest belongs to.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	7/1/2015	7/1/2015	5/1/2018			No			Developed in-house	Inspection data was collected from Plant Protection and Quarantine (PPQ) and Customs and Border Protection (CBP). Quality control of the data was conducted by data analysts. There was no data augmention performed. The data is structured and has several million records with more than 50 numerical and categorical variables. This data is not publicly available.	None;
6	USDA-005: Detection of Pre-symptomatic HLB Infected Citrus	MRP: Marketing and Regulatory Programs	APHIS: Animal and Plant Health Inspection Service	Plant Protection and Quarantine	Science & Space	The purpose of the model is to detect citrus trees infected with Huanglongbing (HLB) disease using images collected by a camera sensor on a small drone. This system would decrease time and cost associated with manual searching for HLB infected trees.	The model outputs GPS Coordinates of potential Huanglongbing (HLB) infected areas.	Stage 5 - Retired (Use case has been retired or is in the process of being retired)	Neither				9/30/2022						No agency-owned data used in this project.
7	USDA-006: High Throughput Phenotyping in Citrus Orchards	MRP: Marketing and Regulatory Programs	APHIS: Animal and Plant Health Inspection Service	Plant Protection and Quarantine	Science & Space	The main purpose of this system is to analyze drone images to locate, count, and categorize citrus trees in an orchard to monitor orchard health. This use case saves thousands of man-hours searching for signs of plant damage and disease in orchards.	The model output flags images containing plant damage or disease.	Stage 5 - Retired (Use case has been retired or is in the process of being retired)	Neither				9/30/2022						No agency-owned data used in this project.
8	USDA-007: Detection of Aquatic Weeds	MRP: Marketing and Regulatory Programs	APHIS: Animal and Plant Health Inspection Service	Plant Protection and Quarantine	Science & Space	The purpose of this system is to locate and identify aquatic weed species using images from drones. Expected benefits include decreasing time that would have been spent manually reviewing the images.	The model outputs the aquatic weed species contained in the image.	Stage 5 - Retired (Use case has been retired or is in the process of being retired)	Neither				9/30/2022						No agency-owned data used in this project.
9	USDA-008: Automated Detection & Mapping of Host Plants from Ground Level Imagery	MRP: Marketing and Regulatory Programs	APHIS: Animal and Plant Health Inspection Service	Plant Protection and Quarantine	Science & Space	This system generates maps of specific tree species from ground-level (streetview) images. Expected benefits are decreased time and cost associated with manual collection of the data.	The model outputs GPS coordinates of flagged locations.	Stage 5 - Retired (Use case has been retired or is in the process of being retired)	Neither				9/30/2022						No agency-owned data used in this project.
10	USDA-009: Democratizing Data	REE: Research, Education, and Economics	NASS: National Agricultural Statistics Service	Strategic Planning and Business Services Division	Mission-Enabling (Internal Agency Support)	This system scans collections of published documents to find how publicly-funded data and evidence are used to serve science and society. This helps the National Agricultural Statistics Service and the Economic Research Service understand who is using their data and why. This improves customer service, helps evaluate programs, and answers important questions for planning and learning.	The model outputs text containing the identified dataset reference information.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	3/8/2021	6/3/2021	7/30/2021		https://github.com/democratizingdata/democratizingdata-ml-algorithms	No			Developed with contracting resources	The model is trained on publicly available peer reviewed research (text data) and known usages of datasets.	None;
11	USDA-011: Land Change Analysis Tool (LCAT)	FPAC: Farm Production and Conservation	FPAC-BC: FPAC Business Center	Geospatial Enterprise Operations	Mission-Enabling (Internal Agency Support)	The Land Change Analysis Tool (LCAT) creates high resolution maps to help make land use decisions. For example, it has been used to monitor eastern redcedar for about 40 years in South Dakota and to support wildlife hazard assessments at airports with various organizations. This tool reduced the labor hours needed by the Farm Service Agency (FSA) to review land data accuracy in Georgia by 100 times.	The model outputs land cover maps.	Stage 3 - Implementation (Use case is currently undergoing functionality and security testing)	Neither	10/1/2018	10/1/2018				No			Developed in-house	The dataset is created from the National Agriculture Imagery Program, which contains images of land taken from aircrafts.	None;
12	USDA-012: OCIO/CDO Council Comment Analysis Tool	DASO: Dept Admin and Staff Offices	OCIO: Office of the Chief Information Officer	Office of the Chief Data Officer	Mission-Enabling (Internal Agency Support)	This prototype helps reviewers identify the main topics and themes of comments, and then group similar comments together. This makes the comment review process more efficient by providing new insights and speeding up comment processing. Benefits include reducing repeated development efforts across the government and saving costs.	The model outputs groups of comments categorized by topic and similarity.	Stage 2 - Development and Acquisition (AI use case is currently under development with the necessary IT tools and data infrastructure having been provisioned)	Neither	12/1/2020	1/1/2021				No			Developed with contracting resources	The training data contains publicly available comment data exported from Regulations.gov. The data largely contains text; some data samples could contain images or data tables.	None;
13	USDA-013: Retailer Receipt Analysis	FNCS: Food, Nutrition, and Consumer Services	FNS: Food and Nutrition Service	Office of Retailer Operations & Compliance	Government Services (includes Benefits and Service Delivery)	This system uses optical character recognition (OCR) to convert physical inventory documentation into digital text. This makes the review of inventory documents more efficient and consistent.	The model outputs digital text of inventory documentation and distinguishes food items and categories.	Stage 2 - Development and Acquisition (AI use case is currently under development with the necessary IT tools and data infrastructure having been provisioned)	Neither	10/1/2021	10/1/2021				No			Developed with contracting resources	The dataset is a collection of Supplemental Nutrition Assistance Program (SNAP) retailer inventory documentation submitted in response to charges of program violations.	None;
14	USDA-014: Ecosystem Management Decision Support System (EMDS)	NRE: Natural Resources and Environment	FS: Forest Service	Research and Development	Energy & the Environment	This system provides decision support for environmental analysis and planning by using AI-powered tools in ArcGIS and QGIS. Use of this system empowers stakeholders to make more informed and effective decisions about natural resource management.	Outputs from this system include the identification of landscapes in need of management/maintenance, along with suggested management actions based on considerations such as cost, efficacy, and policy.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	1/1/1994	6/1/1995	2/1/1997			No			Developed with contracting resources	Primary data sources included observed insect and pathogen-induced mortality, key critical loads for soil and the atmosphere, long term seasonal departures in temperature and precipitation, road densities, uncharacteristic wildfires, historical fire regime departure, wildfire potential, insect and pathogen risk, and vegetation departure from natural range of variability.	None;
15	USDA-016: Cross-Laminated Timber (CLT) Knowledge Database	NRE: Natural Resources and Environment	FS: Forest Service	Northern Research Station	Energy & the Environment	This system enables researchers, practitioners, and the public to find specialized information about timber products. Benefits include faster information sharing and less time spent on manual searches.	System outputs are webpage links from the timber knowledge database.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	12/1/2017	12/1/2017	6/1/2018			No			Developed with contracting resources	Public domain agency publications were used for the dataset. This includes station papers, research, and journal papers by US Forest Service and other scientists.	None;
16	USDA-017: Raster Tools	NRE: Natural Resources and Environment	FS: Forest Service	Rocky Mountain Research Station	Science & Space	This system will make machine learning techniques available for geospatial applications. Benefits include standardization of methods, improved work quality, and increased user productivity.	The system API (Application Programming Interface) provides various AI outputs, usually in the form of raster images and data tables.	Stage 3 - Implementation (Use case is currently undergoing functionality and security testing)	Neither	8/1/2021	8/1/2021				No			Developed in-house	No agency-owned data used in this project.	None;
17	USDA-018: TreeMap and FuelMap (all versions)	NRE: Natural Resources and Environment	FS: Forest Service; Forest Service Research & Development	Rocky Mountain Research Station, Missoula Fire Sciences Lab	Energy & the Environment	TreeMap provides a detailed model of the forests in the US. It is used for measuring carbon, planning fuel treatments, starting landscape vegetation models, assessing fire effects, and more. Users include the US Forest Service, private companies, and state governments.	TreeMap produces a detailed map of a plot of forest and a database table listing individual tree records or fuel characteristics for each plot.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	1/1/2010	10/12/2016	10/1/2018		https://github.com/firelab	No			Developed in-house	TreeMap is validated using Forest Inventory and Analysis field plot data, consisting of percent forest cover, height, vegetation type, topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape around 2016.	None;
18	USDA-019: Landscape Change Monitoring System (LCMS)	NRE: Natural Resources and Environment	FS: Forest Service	Geospatial Office	Science & Space	This project monitors large areas for changes in land cover and land use over time. The benefits include creating a consistent method for tracking changes in the landscape.	The model outputs predictions of vegetation gain, vegetation loss, land cover, and land uses.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	1/15/2016	1/1/2017	3/1/2021			No			Developed in-house	Training and evaluation data used includes 15,000 sample plots spanning from 1985 - 2019. Features of the dataset are land cover, land use, and change process categories. Data is available upon request.	None;
19	USDA-021: Forest Health Detection Monitoring	NRE: Natural Resources and Environment	FS: Forest Service	Geospatial Office	Energy & the Environment	This project monitors forest health by detecting tree damage through changes in light patterns collected by satellites. This detection method helps the Forest Health Protection program monitor areas that can't be checked on the ground or with aerial surveys.	The model outputs the stage of forest health based on the image, along with a map (polygons) of the area for monitoring.	Stage 3 - Implementation (Use case is currently undergoing functionality and security testing)	Neither	6/29/2021	11/18/2021				No			Developed with both contracting and in-house resources	Training data was collected from the National Agriculture Imagery Program (NAIP) and WorldView imagery. Similar high resolution imagery has been used in evaluation and validation. There are approximately 1000 labeled images in the training dataset, each containing spectral data. The data are not publicly available.	None;
20	USDA-022: Cropland Data Layer	REE: Research, Education, and Economics	NASS: National Agricultural Statistics Service	Research and Development Division	Agricultural Statistics	This project produces supplemental estimates of crop acreage and releases geospatial data products to the user community.	The system outputs are an acreage estimate and agrigulture-specific land cover product.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	1/1/2008	1/1/2008	1/1/2008			No			Developed in-house	USDA/Farm Service Agency Common Land Unit data, consisting of Geographic Information System (GIS) shapefiles and associated attribute data, was used for training and testing.	None;
21	USDA-023: List Frame Deadwood Identification	REE: Research, Education, and Economics	NASS: National Agricultural Statistics Service	Frames Maintenance	Agricultural Statistics	This model helps identify farms that may be out of business on the National Agricultural Statistics Service list. Parts of the model were used to create clear rules to identify these farms. The resulting list is more accurate and allows for smaller sample sizes, reducing the burden on respondents.	The output of the model was a probability score that a farm is out of business.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	2/4/2014	2/3/2016	1/24/2018			No			Developed in-house	National Agricultural Statistics Service List frame and survey data, consisting of features put together to form profiles of farming operations, was used to train and validate the model.	Age;
22	USDA-024: Climate Change Classification NLP	REE: Research, Education, and Economics	NIFA: National Institute of Food and Agriculture	Planning, Accountability and Reporting Staff and Institute of Bioenergy, Climate and Environment	Mission-Enabling (Internal Agency Support)	The Climate Change Classification Natural Language Processing (NLP) model identifies likely climate-related projects within National Institute of Food and Agriculture's (NIFA) large and diverse funding portfolio. Expected benefits include reduced labor hours for reporting and increased repeatability and accuracy of reporting.	Model output is a list of climate change projects classified as "climate change related" or "not climate change related" for National Institute of Food and Agriculture (NIFA) internal project review/adjudication and reporting.	Stage 2 - Development and Acquisition (AI use case is currently under development with the necessary IT tools and data infrastructure having been provisioned)	Neither	7/1/2021	7/1/2024				No			Developed with both contracting and in-house resources	This model is trained on publicly available project information provided by the project directors via National Institute of Food and Agriculture's (NIFA) REEport reporting portal. This includes text fields containing the project's title, summary, objectives, and keywords. The training data is catagorized by the project team.	None;
23	USDA-025: Video Surveillance System	DASO: Dept Admin and Staff Offices	OSSP: Office of Safety, Security, and Protection	Facility Protection Division (FPD)	Mission-Enabling (Internal Agency Support)	The purpose of this system is to conduct facial recognition video surveillance to provide enhanced security. Benefits include reduced labor hours for technicians and augmented surveillance capability.	The system outputs a positive match to the security control center, indicating identification of the selected individual. An alarm notification is sent to alert security personnel.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Rights-Impacting	2/14/2020	2/14/2020	2/14/2021			No			Developed with both contracting and in-house resources	No agency-owned data used in this project.	Sex/Gender; Race/Ethnicity;
24	USDA-026: Aquisition Approval Request Compliance Tool	DASO: Dept Admin and Staff Offices	OCIO: Office of the Chief Information Officer	Office of the Chief Information Officer	Mission-Enabling (Internal Agency Support)	This project was developed to help identify likely Information Technology (IT) purchases that do not have an associated Acquisition Approval Request. The benefits are reducing unauthorized IT purchases and increasing compliance with IT procurement procedures and approvals.	The output is a score indicating how likely it is that the purchase is an Information Technology (IT) purchase.	Stage 2 - Development and Acquisition (AI use case is currently under development with the necessary IT tools and data infrastructure having been provisioned)	Neither	10/31/2019	4/6/2020				No			Developed with both contracting and in-house resources	The model was developed using text entries entered into USDA's Integrated Acquisition System (IAS).	None;
25	USDA-027: Operational Water Supply Forecasting for Western US Rivers	FPAC: Farm Production and Conservation	NRCS: Natural Resources Conservation Service	National Water and Climate Center	Mission-Enabling (Internal Agency Support)	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.	The model outputs water supply forecasts.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	12/1/2019	12/1/2019	1/1/2024			Yes	Natural Resources Conservation Service	Environmental Quality Incentives Program (EQIP)	Developed in-house	The data contains snow and precipitation metrics from the Natural Resources Conservation Service (NRCS) Snow Survey and Water Supply Forecast program Snow Telemetry (SNOTEL) monitoring network.	None;
26	USDA-028: Standardization of Cut Flower Business Names for Message Set Data	MRP: Marketing and Regulatory Programs	APHIS: Animal and Plant Health Inspection Service	Plant Protection and Quarantine	Mission-Enabling (Internal Agency Support)	Natural Language Processing (NLP) is used to match names of producers to varietal information for cut flowers, which will help convert from manual to automated inspection systems. The main benefit of automation is that it can manage thousands of entities, which would be impossible to handle manually.	The model outputs before and after lists of producer names and cut flower varieties.	Stage 5 - Retired (Use case has been retired or is in the process of being retired)	Neither				1/1/2024						No agency-owned data used in this project.
27	USDA-029: Intelligent Ticket Routing	DASO: Dept Admin and Staff Offices	OCIO: Office of the Chief Information Officer	Digital Infrastructure Services Center	Mission-Enabling (Internal Agency Support)	Help desk tickets are often sent to the wrong group and must be manually re-routed to the correct group, which can take time, resources, and may delay issue resolution. The Intelligent Ticket Routing system helps to send the ticket to the correct group, increasing customer satisfaction by reducing the number of times a customer is transferred or placed on hold, and decreasing the average handle time (AHT). In our specific use case, we reduce the time taken to route a ticket to the appropriate group, shortening the time required to resolve an issue.	The system outputs a prediction of the appropriate group for ticket management.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	1/1/2022	1/1/2022	1/1/2022			No			Developed with both contracting and in-house resources	The data is a collection of scripts from Remedy reporting database, a structured database containing freeform text fields. More than 100,000 samples and 526 features comprise the data, including numerical, categorical, text, and date types. Data is not public.	None;
28	USDA-030: Predictive Maintenance Impacts	DASO: Dept Admin and Staff Offices	OCIO: Office of the Chief Information Officer	Digital Infrastructure Services Center	Mission-Enabling (Internal Agency Support)	A natural language processing (NLP) model classifies whether infrastructure maintenance changes will or will not cause an incident at the Digital Infrastructure Services Center (DISC). Using this system, the business can improve the review process or address specific needs within groups. This will lead to process improvements, increased productivity, higher performance and job satisfaction, higher client satisfaction, and better achievement of key performance indicators (KPIs).	The model outputs a score between 0 to 1. Closer to 1 indicates higher likelihood of an incident created by the proposed change.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	3/1/2020	3/1/2020	3/1/2020			No			Developed with both contracting and in-house resources	The dataset is composed of Remedy data, containing inventory data of all of assets at DISC: hardware, software, incident tickets, and change tickets. Data contains numerical, categorical, text, and date data types.	None;
29	USDA-031: Artificial Intelligence SPAM Mitigation Project	DASO: Dept Admin and Staff Offices	OASCR: Office of the Assistant Secretary for Civil Rights	Center for Civil Rights Operations; Data Records and Management Division	Government Services (includes Benefits and Service Delivery)	An AI/ML model automatically classifies and removes spam and marketing emails from civil rights complaints email channels. Benefits include reducing the time spent manually managing email channels, decreasing the memory burden on email systems, and lowering the risk from malicious emails.	The model outputs a classification of received emails, flagging spam, marketing, and phishing emails.	Stage 5 - Retired (Use case has been retired or is in the process of being retired)	Neither				9/30/2023						No agency-owned data used in this project.
30	USDA-032: Approximate String Matching (aka fuzzy matching) to Standardize Data	MRP: Marketing and Regulatory Programs	APHIS: Animal and Plant Health Inspection Service	Plant Protection and Quarantine	Mission-Enabling (Internal Agency Support)	A model is used to replace typos in Plant Protection and Quarantine (PPQ) program data using a list of standardized producer and commodity names. This results in clean, standardized data through an automated workflow. Benefits include reducing labor hours compared to manual data cleaning, makes near-real-time reporting possible, and accurate data enables program managers to conduct efficient policy enforcement and program monitoring.	The model outputs corrected text data.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	2/1/2023	2/23/2023	5/16/2023			No			Developed in-house	The data used includes data tables from the Agriculture Risk Management (ARM) data system, Agricultural Commodity Import Requirements (ACIR), and the Participating Government Agencies (PGA) Message Set. The cleaned data is displayed in a dashboard for program monitoring. Quality control data are exported from each matching routine and used to validate results as needed.	None;
31	USDA-033: Automated PDF Document Processing and Information Extraction	MRP: Marketing and Regulatory Programs	APHIS: Animal and Plant Health Inspection Service	Plant Protection and Quarantine	Mission-Enabling (Internal Agency Support)	This use case takes program and workforce related information stored in thousands of PDFs and converts the information into data tables that can be used for analytics and dashboards. This makes information that is difficult to find available in real-time to support decision making and saves large amounts of time compared to previous methods used.	The model outputs structured database tables.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	10/15/2022	10/24/2022	1/15/2023			No			Developed in-house	The data used is from Agriculture Vessel Inspection forms concerning flighted spongy moth complex, sent from Customs and Border Protection to Plant Protection and Quarantine.	None;
32	USDA-035: Census Propensity Scores via ML	REE: Research, Education, and Economics	NASS: National Agricultural Statistics Service	Research and Development Division	Agricultural Statistics	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.	The model outputs a probability score (all values from and including 0 to 1).	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	10/1/2022	10/1/2022	11/1/2022			No			Developed in-house	The data is collected from the 2017 Census of Agriculture, which includes number of farms by size and type, inventory and values for crops and livestock, producer characteristics, and much more.	Zipcode;
33	USDA-036: Ecological Site Descriptions (Machine Learning)	FPAC: Farm Production and Conservation	NRCS: Natural Resources Conservation Service	Soil Science and Resource Assessment	Mission-Enabling (Internal Agency Support)	This AI/ML work conducts analysis of over 20 million records of soils data and 20,000 text documents of ecological information in order to provide complete soil based ecological information for the country. Benefits include reduction in labor hours manually analyzing documents, and enabling stakeholders to examine records in ways previously not thought of to make more informed decisions.	The AI model outputs ecological soil classifications and mappings.	Stage 5 - Retired (Use case has been retired or is in the process of being retired)	Neither				9/30/2023						No agency-owned data used in this project.
34	USDA-037: Conservation Effects Assessment Project	FPAC: Farm Production and Conservation	NRCS: Natural Resources Conservation Service	Resource Inventory and Assessment Division	Mission-Enabling (Internal Agency Support)	The purpose of the use case is to predict the conservation effects of cropland practices in real time, with no technical skill required. Such models would allow field conservation planners to have real-time conservation effects on sediment and nutrients.	The model outputs predictions of sediment and nutrient change values based on conservation methods.	Stage 2 - Development and Acquisition (AI use case is currently under development with the necessary IT tools and data infrastructure having been provisioned)	Neither	11/1/2021	11/1/2021				No			Developed in-house	The data is collected from the Conservation Effects Assessment Project Survey cropland assessment, which measures trends in cropland conservation practices and their outcomes over time.	None;
35	USDA-038: Digital Imagery (no-change) for NRI Program	FPAC: Farm Production and Conservation	NRCS: Natural Resources Conservation Service	Resource Inventory and Assessment Division	Mission-Enabling (Internal Agency Support)	AI algorithms are used to look at landscape images and detect if the landscape has changed from year to year. Currently, about 72000 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.	The model outputs a classification of “no-changes” in the images if the landscape in the images remain stable from year to year.	Stage 1 - Initiation (The use case's intended purpose and high-level requirements are documented)	Neither	10/1/2022									No agency-owned data currently used in this project.
36	USDA-039: Nutrition Education & Local Access Dashboard	FNCS: Food, Nutrition, and Consumer Services	FNS: Food and Nutrition Service	Regional Operations & Support; Mountain Plains Regional Office	Government Services (includes Benefits and Service Delivery)	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, ethnicity and race, 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. Benefits include increasing stakeholder awareness and empowering more informed decision-making and collaboration.	The model outputs groups of similar counties/states based on the different combinations of properties available for states.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	11/9/2022	1/20/2023	6/23/2023			No			Developed with both contracting and in-house resources	The data is composed of numerical and categorical data describing farm to school intensity and size, program activity intensity, ethnicity and race, fresh food access, school size, and program participation.	Race/Ethnicity;
37	USDA-040: Survey Text Remarks Value Scoring	REE: Research, Education, and Economics	NASS: National Agricultural Statistics Service	Methods Division	Mission-Enabling (Internal Agency Support)	The purpose of this use case is to analyze a large amout of text in survey responses and score all comments with a priority value. The highly scored blocks of text then get prioritized for review by a human and are responded to more quickly than if they were to be retained in a queue.	The model outputs a value score on each snippit of text, highly scored snippits of text are placed at the front of the queue before lower scored blocks to capture text of value more quickly.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	9/24/2021	12/27/2021	4/20/2022			No			Developed in-house	The training data is created from historically collected text and survey remarks.	None;
38	USDA-041: Survey Outlier Detection Model	REE: Research, Education, and Economics	NASS: National Agricultural Statistics Service	Methodology Division; Statistics Division; Regional Field Offices	Agricultural Statistics	The purpose of this use case is to identify abnormal values to edit in surveys. This reduces manual labor and improves data quality.	The model outputs a recommendation of which values in a dataset should be changed.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	5/1/2022	5/1/2022	5/27/2022		https://jds-online.org/journal/JDS/article/1383/file/13236	No			Developed in-house	The data is collected from National Agricultural Statistics Service Survey Data, which is structured data containing categorical and numerical values.	None;
39	USDA-042: Multilingual Translation of Recalls and Public Health Alerts	Food Safety	FSIS: Food Safety and Inspection Service	Office of the Chief Technology Officer	Government Services (includes Benefits and Service Delivery)	The purpose of this system is to expand the multilingual outreach of food safety information like recalls and public health alerts. Benefits include cost savings on vendor translation services, faster messaging circulation, and increased number of available languages to the general public.	The model outputs multilingual translations created from the original english text.	Stage 2 - Development and Acquisition (AI use case is currently under development with the necessary IT tools and data infrastructure having been provisioned)	Neither	6/15/2023	6/15/2023				No			Developed with both contracting and in-house resources	No agency-owned data used in this project.	None;
40	USDA-043: Genomic Analyses of Pathogen Subtypes	Food Safety	FSIS: Food Safety and Inspection Service	Office of Public Health Science	Science & Space	The purpose of this use case is to use machine learning (ML) methods to group foodborne germs based on patterns in their genes, then connect this information with available health data to evaluate foodborne germ risk to public health. Expected benefits include improving our understanding of important foodborne germ genes, assessing key genes and new trends, and identifying and ranking germs that are important for public health.	The model outputs predictions of high risk foodborne germ subtypes, key genetic markers by importance, and emerging trends.	Stage 2 - Development and Acquisition (AI use case is currently under development with the necessary IT tools and data infrastructure having been provisioned)	Neither	8/1/2022	2/1/2023				No			Developed in-house	The data is composed of agency-owned data, including whole genome sequencing (WGS) data from Food Safety and Inspection Service (FSIS). Sampling programs are publicly available and hosted on the National Center for Biotechnology Information (NCBI) database.	None;
41	USDA-044: Foodborne Illness Source Attribution	Food Safety	FSIS: Food Safety and Inspection Service	Office of Public Health Science	Science & Space	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 caused by various germs (e.g., Salmonella, Campylobacter). Expected benefits include improving our understanding of where these germs come from and how they spread, which can help in creating measures and policies to prevent or reduce illnesses and the overall impact of these diseases.	The model outputs predictions of likely sources of foodborne human illness cases, along with a confidence score of how probable it is that the illness came from the predicted source.	Stage 2 - Development and Acquisition (AI use case is currently under development with the necessary IT tools and data infrastructure having been provisioned)	Neither	8/2/2021	2/1/2023				No			Developed in-house	The data is composed of agency-owned data, including whole genome sequencing (WGS) data from Food Safety and Inspection Service (FSIS). Sampling programs are publicly available and hosted on the National Center for Biotechnology Information (NCBI) database.	None;
42	USDA-045: Public Comments Analysis	MRP: Marketing and Regulatory Programs	APHIS: Animal and Plant Health Inspection Service; AMS: Agricultural Marketing Service	MRPIT Data & Analytics Directorate	Mission-Enabling (Internal Agency Support)	The purpose of the model is to automate the analysis of comments from regulations.gov to help personnel in their review and response tasks. Benefits include a reduction in the number of labor hours needed for review and response.	The model outputs text analysis and categorization of the public comments.	Stage 2 - Development and Acquisition (AI use case is currently under development with the necessary IT tools and data infrastructure having been provisioned)	Neither	11/1/2023	11/1/2023				No			Developed in-house	No agency-owned data used in this project.	None;
43	USDA-046: Rangeland Analysis Platform	REE: Research, Education, and Economics	ARS: Agricultural Research Service	Rangeland Management Research Unit (Las Cruces)	Energy & the Environment	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 for wildlife habitats, carbon assessments, and tax assessments.	The system outputs estimated fractional plant cover and net primary productivity estimates.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	4/1/2022	3/1/2023	1/1/2017			No			Developed in-house	The data is collected from over 75,000 monitoring locations collected by Natural Resources Conservation Service (NRCS), Bureau of Land Management, and other agencies. These data were combined and stored in the Landscape Data Commons (www.landscapedatacommons.org).	None;
44	USDA-047: Predictive Cropland Data Layer	REE: Research, Education, and Economics	NASS: National Agricultural Statistics Service	Research and Development Division; Methodology Division; Regional Field Offices	Agricultural Statistics	The purpose of this system is to predict crop rotations. Benefits include improving data quality of area-based surveys.	The system outputs predictions of the types of crops in specific locations within the Conterminous United States (CONUS).	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	1/1/2021	1/1/2021	2/15/2021			No			Developed in-house	The data is derived from Cropland Data Layer (CDL) and Farm Service Agency (FSA) administrative data. CDL is produced using satellite images and extensive agricultural ground reference data.	None;
45	USDA-048: Dam Inspection Report Document Processing	FPAC: Farm Production and Conservation	NRCS: Natural Resources Conservation Service	Oklahoma Natural Resources Conservation Service (NRCS) Watershed Program	Mission-Enabling (Internal Agency Support)	The purpose of this AI is to pull out and organize data from thousands of dam inspection documents so that we can use Microsoft Power BI to understand the condition of thousands of USDA Watershed program dams. This allows us to identify the biggest issues and trends across our collection of over 2,100 dams in Oklahoma while reducing labor hours required to complete the task manually.	The model outputs text and checkbox responses, including dam metadata, inspection issue tracking (yes and no checkboxes), and further remarks on the issue or what has been/needs to be done on the dam.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	5/1/2023	9/1/2023	12/1/2023			No			Developed in-house	The data is made up of dam inspection forms collected by dam owners (ex. conservation districts, cities) or Natural Resources Conservation Service (NRCS) staff. The data was validated through manual checks and is composed of 30+ dam inspection reports with different handwriting. The data contains 120+ variables that are read from each two-page inspection report. The data types are date, text, table (text variable types in the table), and checkbox binary outputs (checked: yes or no). The data is not publicly available.	None;
46	USDA-049: Portfolio Approval and Management (PAM) Bot	REE: Research, Education, and Economics	ERS: Economic Research Service	Information Services Division	Mission-Enabling (Internal Agency Support)	The purpose of the model is to improve the Economic Research Service (ERS) research approval process. The system reduces the time it takes to fill out information and seeking approval, improves information accuracy, and brings visibility to the approval status across various division functions.	The system provides three outputs: the approval status, summary recommendations, and generated citations.	Stage 1 - Initiation (The use case's intended purpose and high-level requirements are documented)	Neither	5/1/2024									No agency-owned data used in this project.
47	USDA-050: GIS Invasive Tree Extraction for Field Level Users	FPAC: Farm Production and Conservation	NRCS: Natural Resources Conservation Service	Nebraska Natural Resources Conservation Service (NRCS)	Energy & the Environment	The puspose of the model is to estimate of the spread of invasive tree infestation, specifically Eastern redcedar. This helps to avoid poor or inaccurate estimates caused by time constraints and heavy workloads when manually collecting the data.	The model outputs polygons representing the extent of trees present in landscape.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	7/15/2019	7/15/2019	9/30/2019			No			Developed in-house	The data is created from the National Agriculture Imagery Program (NAIP), aerial images of land taken from aircrafts. The data is not limited to NAIP, but the 2020 NAIP has proven to be the most reliable source data.	None;
48	USDA-051: DISTRIB-II: Habitat Suitability of Eastern United States Tree	NRE: Natural Resources and Environment	FS: Forest Service	Northern Research Station	Science & Space	The purpose and expected benefits of the Climate Change Atlas are to give forest resource managers, forest landowners, and the general public information on the current and potential future of habitats for various tree species in the eastern United States. This information can contribute to forest management decisions when considering how climate change may affect the trees currently present and how likely it is that other tree species not currently in an area might find new habitats under different climate change scenarios.	The system outputs predictions of how well a tree species can live in a certain habitat based on climate change scenarios. Maps, graphs, and reports are generated from the modeled geographic information systems (GIS) data.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	4/10/1998	5/16/2016	9/12/2019			No			Developed in-house	The data is created from publicly available Forest Inventory and Analysis data.	None;
49	USDA-052: FSA FLP Chatbot	FPAC: Farm Production and Conservation	FSA: Farm Service Agency	Assistant Chief Data Officers Team	Mission-Enabling (Internal Agency Support)	The purpose of this use case is to solve the problem of searching Loan handbooks to provide better customer service. The expected benefit is to help employees provide better service. A second benefit that is being explored is providing Veteran specific answers to services.	The expected output is text answers to prompt questions.	Stage 2 - Development and Acquisition (AI use case is currently under development with the necessary IT tools and data infrastructure having been provisioned)	Neither	12/14/2023	3/29/2024				No			Developed in-house	The data is collected from the 1 and 3 Farm Loan handbooks that are available on USDA intranet. The handbooks contain text, tables, and charts. There are also images such as screen shots in the handbooks.	Veteran;
50	USDA-053: ROE Document Recognition - RoeDR	FPAC: Farm Production and Conservation	RMA: Risk Management Agency	Insurance Services	Mission-Enabling (Internal Agency Support)	The purpose of this model is to analyze documents from producers and Authorized Insurance Providers (AIPs), pick the appropriate page from the documents, read the signature date and producer signature name, convert the date and name to text, and load it into an application. This feature saves us time from having to input the data manually. We can then use the data for reporting purposes.	The model outputs the producer signature and signature date within document as text.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	5/15/2024	5/23/2024	6/12/2024			No			Developed in-house	The data is created from actuarial change forms and determined yield requests received from authorized insurance providers. The data contained 150 documents.	None;
51	USDA-054: IRIS	MRP: Marketing and Regulatory Programs	APHIS: Animal and Plant Health Inspection Service	Biotechnology Regulatory Services	Science & Space	The purpose of this system is to make literature searches more effective for Biotechnology Regulatory Services. This increases work efficiency with our regulatory tasks.	The model outputs recommended literature list for scientists.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	1/9/2023	2/5/2024	5/15/2024			No			Developed with contracting resources	No agency-owned data used in this project.	None;
52	USDA-055: Ticket Resolution Categorization (Incident/Change)	DASO: Dept Admin and Staff Offices	OCIO: Office of the Chief Information Officer	Digital Infrastructure Services Center	Mission-Enabling (Internal Agency Support)	The purpose of this model is to classify the resolution type and tier of all support desk tickets after they have been closed. This model allows the support team to spend more time identifying process inefficiencies and plan solutions rather than categorizing tickets. This will lead to process improvement, automation of repetitive tasks, increased productivity, and higher performance.	The model outputs the classification category of support ticket resolutions.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	6/1/2023	6/1/2023	6/1/2023			No			Developed with both contracting and in-house resources	The dataset is composed of Remedy data, containing inventory data of all of assets at DISC: hardware, software, incident tickets, and change tickets. Data contains numerical, categorical, text, and date data types.	None;
53	USDA-056: Ticket Templatization	DASO: Dept Admin and Staff Offices	OCIO: Office of the Chief Information Officer	Digital Infrastructure Services Center	Mission-Enabling (Internal Agency Support)	This model is a non-production exploratory model, meaning it does not make predictions but is used to explore trends within data to gain insights that can help in making data-driven decisions. It is designed to explore and analyze service and change requests submitted through the 105 general form or without templates. This model helps to identify subcategories within the larger dataset that could be candidates for standardization and automation, potentially leading to improved operational efficiency, cost savings, and customer satisfaction.	The model outputs trends within data to assist in decision making.	Stage 2 - Development and Acquisition (AI use case is currently under development with the necessary IT tools and data infrastructure having been provisioned)	Neither	1/1/2024	1/1/2024				No			Developed with contracting resources	The dataset is composed of Remedy data, containing inventory data of all of assets at DISC: hardware, software, incident tickets, and change tickets. Data contains numerical, categorical, text, and date data types.	None;
54	USDA-057: File Rename Automation	FPAC: Farm Production and Conservation	FSA: Farm Service Agency	North Dakota State Office	Mission-Enabling (Internal Agency Support)	The purpose of this tool is to rename thousands of documents converted from physical to digital records that were given a generic file name. This tool can grab text from page 1 of each document and apply a correct file rename instead of employees having to spend hours manually renaming documents.	The model outputs renamed files.	Stage 3 - Implementation (Use case is currently undergoing functionality and security testing)	Neither	11/6/2023	11/6/2023				No			Developed in-house	The data is created from agency records and were used to train Microsoft Power Automate to extract data from records and use in the file renaming process.	None;
55	USDA-058: Rapid Drafting of ARS Research Summaries	REE: Research, Education, and Economics	ARS: Agricultural Research Service	Office of National Programs	Mission-Enabling (Internal Agency Support)	The purpose of this tool is to summarize ongoing research from internal Agricultural Research Service (ARS) documents to allow program staff to quickly create accurate and timely summary documents, such as briefing papers, talking points for leadership, and speeches. This will give staff more time for other duties, and leadership will be able to confidently answer questions, justify budget requests, and ensure that our research is innovative and relevant.	The tool outputs talking points and short briefing papers.	Stage 1 - Initiation (The use case's intended purpose and high-level requirements are documented)	Neither	3/18/2024									No agency-owned data used in this project.
56	USDA-059: DS Hub Geo-metadata generation	FPAC: Farm Production and Conservation	NRCS: Natural Resources Conservation Service	Soil and Plants Science Division; Soil Services and Information; Conservation Information Delivery	Government Services (includes Benefits and Service Delivery)	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. Expected benefits include increased data accessibility, reduced manual workload, minimized errors, better dataset understanding, and fast data retrieval for stakeholders.	The model outputs accurate, consistent, and compliant metadata appropriate for the existing geospatial data that it analyzed.	Stage 1 - Initiation (The use case's intended purpose and high-level requirements are documented)	Neither	9/20/2024									No agency-owned data used in this project.
57	USDA-060: Dynamic Soils Hub	FPAC: Farm Production and Conservation	NRCS: Natural Resources Conservation Service	Soil and Plants Science Division; Soil Services and Information; Conservation Information Delivery	Science & Space	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. The DS Hub links different soil and conservation databases, making it easier to evaluate the environmental benefits of conservation programs by accessing previously separate data and models. This enhances the USDA’s ability to study and report on how soil properties change with conservation efforts over time.	The system outputs the class of soil based on the supplied soil information.	Stage 3 - Implementation (Use case is currently undergoing functionality and security testing)	Neither	11/11/2020	11/11/2020				No			Developed with contracting resources	No agency-owned data used in this project.	None;
58	USDA-061: Cover Crop Mapping	FPAC: Farm Production and Conservation	RMA: Risk Management Agency	Deputy Administrator for Compliance; Business Analytics Division	Law & Justice	This project aims to annually map fall and spring cover crop practices on farms in the U.S. Midwest. These maps are made using satellite images and models of plant growth. This data helps the agency independently find out the extent of cover crop practices.	The output is a state-level map of detected cover crops by year, classified by planting date (fall, spring).	Stage 2 - Development and Acquisition (AI use case is currently under development with the necessary IT tools and data infrastructure having been provisioned)	Rights-Impacting	9/2/2022	9/2/2022				No			Developed with both contracting and in-house resources	The data is created from satellite imagery that uses crop insurance cover crop subsidy data as an independent data validation source. Data is not publicly available.	None;
59	USDA-062: Planting Date Detection	FPAC: Farm Production and Conservation	RMA: Risk Management Agency	Deputy Administrator for Compliance; Business Analytics Division	Law & Justice	This project aims to find out the planting dates for corn, soybean, and winter wheat on farms in the U.S. Midwest. Maps containing planting dates are made using satellite images and models of plant growth. This data helps the agency independently verify reported planting dates on farm fields supporting efforts to ensure the integrity of their programs.	The output is an annual map of planting dates for corn, soybean, and winter wheat for crop years 2016-2023.	Stage 2 - Development and Acquisition (AI use case is currently under development with the necessary IT tools and data infrastructure having been provisioned)	Rights-Impacting	9/7/2022	9/1/2023				No			Developed with both contracting and in-house resources	The data is created from satellite imagery that uses crop insurance unit-level planting dates at the crop/type/practice level. Data is not publicly available.	None;
60	USDA-063: Acreage and Crop Type Validation	FPAC: Farm Production and Conservation	RMA: Risk Management Agency	Deputy Administrator for Compliance; Business Analytics Division	Law & Justice	This project uses satellite images and plant growth models to generate a farm field size estimate and the crop type on farms in the U.S. Midwest. This data helps the agency independently find out the accuracy of reported field sizes and crop types, supporting efforts to ensure the integrity of their programs.	The output is a validation of reported acreage and validation of reported crop type for corn, soybean, and winter wheat on farm fields.	Stage 2 - Development and Acquisition (AI use case is currently under development with the necessary IT tools and data infrastructure having been provisioned)	Rights-Impacting	9/1/2022	9/1/2022				No			Developed with both contracting and in-house resources	The data is created from satellite imagery that uses planted field size and crop type (corn, soybean, and winter wheat) features as an independent data validation source. Data is not publicly available.	None;
61	USDA-064: U.S. Poultry Operations and Populations Dataset	MRP: Marketing and Regulatory Programs	APHIS: Animal and Plant Health Inspection Service	Veterinary Services	Emergency Management	The purpose of this case is to develop a dataset that addresses the problem of not having complete information about where poultry farms are located and how many birds they have. Filling this gap provides detailed data on poultry farm locations and populations, which is essential for planning animal health emergencies and predicting the spread of diseases.	Output is a national-level dataset of domestic poultry operations and estimated populations.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	5/15/2016	5/15/2016	6/3/2019			No			Developed in-house	The data consists of a subset of farm locations that were validated using manually digitized poultry operations in 41 counties. Data is not publicly available.	None;
62	USDA-065: Equine Operations and Populations Dataset for the U.S.	MRP: Marketing and Regulatory Programs	APHIS: Animal and Plant Health Inspection Service	Veterinary Services	Emergency Management	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. Filling this gap provides detailed data on horse farm locations and populations, which is essential for planning emergencies and predicting the spread of diseases.	Output is a national-level dataset of domestic horse operations and estimated populations.	Stage 2 - Development and Acquisition (AI use case is currently under development with the necessary IT tools and data infrastructure having been provisioned)	Neither	1/2/2023	9/1/2024				No			Developed with both contracting and in-house resources	The data is created from National Agriculture Imagery Program (NAIP) images and the National Agricultural Statistics Service Census of Agriculture, which includes number of farms by size and type, inventory and values for crops and livestock, producer characteristics, and much more. Data is not publicly available.	None;
63	USDA-066: NASS - Naggle 2.0 Automated Editing Tool	REE: Research, Education, and Economics	NASS: National Agricultural Statistics Service	National Agricultural Statistics Service	Agricultural Statistics	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 regression model will then suggest a corrected value. This approach will help reduce errors and improve the accuracy of survey forms, saving time and reducing the number of labor hours spent on editing.	The classification model outputs an excel sheet with the survey, person, variable, and whether the variable is valid or invalid. The regression model outputs an excel sheet containing the invalid records, which includes the survey, person, variable, original value, and new predicted value.	Stage 1 - Initiation (The use case's intended purpose and high-level requirements are documented)	Neither	6/3/2024									No agency-owned data currently used in this project.
64	USDA-067: County-level remotely-sensed corn and soybean yield estimation	REE: Research, Education, and Economics	NASS: National Agricultural Statistics Service	Research and Development Division	Agricultural Statistics	The purpose of this tool is to estimate yearly corn and soybean yields for each county using satellite images. More details can be found in the paper titled “An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States” (https://doi.org/10.1016/j.rse.2013.10.027). Benefits of providing county-level crop yield statistics allow stakeholders to make more informed planning and decisions.	The model outputs county-level crop yield estimates for corn and soybeans in the amount of bushels per acre.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	1/1/2007	1/1/2009	1/1/2014			No			Developed in-house	Historical National Agricultural Statistics Service county-level crop yields are used as the dataset. The foundation of the data is images from NASA Moderate Resolution Imaging Spectroradiometer (MODIS) satellites.	None;
65	USDA-068: NASSportal Intranet Bot	REE: Research, Education, and Economics	NASS: National Agricultural Statistics Service	Strategic Planning and Business Services Division	Mission-Enabling (Internal Agency Support)	This model will assist National Agricultural Statistics Service (NASS) staff in finding answers to questions on how to administer programs. This will decrease labor hours and increase efficiency of NASS staff in program administration.	The chatbot will provide text outputs.	Stage 1 - Initiation (The use case's intended purpose and high-level requirements are documented)	Neither	7/1/2024									No agency-owned data used in this project.
66	USDA-069: XyloTron/XyloPhone Wood Identification System	NRE: Natural Resources and Environment	FS: Forest Service	Research and Development; Forest Products Laboratory	Law & Justice	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.	The tools will output a prediction of the type wood.	Stage 3 - Implementation (Use case is currently undergoing functionality and security testing)	Neither	1/1/2016	1/1/2016				No			Developed in-house	The data contains images of the cross-section of polished wood surfaces. As of July 2024, there are 139,639 images of 10,888 wood specimens.	None;
67	USDA-070: Incident Invoice Document Understanding	NRE: Natural Resources and Environment	FS: Forest Service	Procurement and Property Services	Mission-Enabling (Internal Agency Support)	The purpose of this tool is to analyze incident invoices and return the values that need to be entered into a database. This new approach leads to faster invoice processing and reduces data entry mistakes for more accurate data.	The tool outputs an Excel document containing required values identified from incident invoices.	Stage 2 - Development and Acquisition (AI use case is currently under development with the necessary IT tools and data infrastructure having been provisioned)	Neither	1/21/2021	1/26/2021				No			Developed with contracting resources	The dataset is created with PDF files of OF-286 documents, which are paid invoices of varying quality and structure.	None;
68	USDA-071: Forest disease detection and screening	NRE: Natural Resources and Environment	FS: Forest Service	Northern Research Station	Science & Space	The purpose of this project is to improve tree disease diagnosis and screening, thereby facilitating ongoing efforts within and outside the Forest Service to manage diseases of forest trees.	The model will output a prediction indicating whether a tree is diseased or not, and if a tree is resistant or susceptible to a disease.	Stage 2 - Development and Acquisition (AI use case is currently under development with the necessary IT tools and data infrastructure having been provisioned)	Neither	8/3/2020	8/3/2020				No			Developed in-house	Data has and is currently being collected using commercially available and custom sensors and images from trees in natural forest landscape, trees in planted settings, and trees that are being screened for disease resistance in controlled environments. Data is collected for both model calibration and validation.	None;
69	USDA-072: Use of LLMs for data extraction	NRE: Natural Resources and Environment	FS: Forest Service	Northern Research Station	Mission-Enabling (Internal Agency Support)	The purpose of this model is to quickly gather information from scientific papers to track plant diseases. The practical benefit is that using this method would save time compared to manually collecting the information, which can be slow and error-prone when done for a long time.	The model outputs a table of requested data variables (e.g., country, pathogen name, host name, etc.).	Stage 1 - Initiation (The use case's intended purpose and high-level requirements are documented)	Neither	10/1/2023									No agency-owned data used in this project.
70	USDA-073: IPWG Application Survey Analysis	NRE: Natural Resources and Environment	FS: Forest Service	Business Operations/Chief Data Office	Mission-Enabling (Internal Agency Support)	The purpose of this tool is to analyze over 5,000 responses from an internal employee survey on IT applications, then give a summary of the employee feedback regarding each IT application. This decreases the time required to go through each response manually, helping the team make informed investment decisions more quickly.	The model outputs a text summarization for each IT application in the survey data, and potentially includes text summaries of responses and sentiment analysis. The project will also produce a dashboard that allows users to see similar attributes at the agency, office, application, and individual response level.	Stage 2 - Development and Acquisition (AI use case is currently under development with the necessary IT tools and data infrastructure having been provisioned)	Neither	10/3/2024	10/3/2024				No			Developed in-house	The data has been collected from a Microsoft Forms survey sent to Forest Service employees and downloaded into a CSV (comma-separated values) file. The survey responses are not anonymous and contain personnel information such as region, office, and job title. There are approximately 5000 responses about hundreds of applications. The data is comprised of approximately 54 attributes, with a mix of numerical ratings, categories, and free text comments. The data is restricted and encrypted due to personnel information.	None;
71	USDA-074: Fire Resilent Landscapes	NRE: Natural Resources and Environment	FS: Forest Service	Rocky Mountain Research Station	Science & Space	The goal of this tool is to quantify the cost of forest treatments. Benefits include providing the ability to accurately map treatment costs for users to make more informed decisions.	The tool outputs predictions in the form of raster surfaces/maps.	Stage 3 - Implementation (Use case is currently undergoing functionality and security testing)	Neither	8/1/2021	8/1/2021				No			Developed in-house	No agency-owned data used in this project.	None;
72	USDA-075: PC Rasterize	NRE: Natural Resources and Environment	FS: Forest Service	Rocky Mountain Research Station	Science & Space	The purpose of this tool is to be able to process point cloud data more efficiently. This will reduce costs associated with processing point cloud data.	The tool outputs point clouds and raster surfaces/maps.	Stage 3 - Implementation (Use case is currently undergoing functionality and security testing)	Neither	8/1/2024	8/1/2024				No			Developed in-house	No agency-owned data used in this project.	None;
73	USDA-076: Spread and Balance Sample Design	NRE: Natural Resources and Environment	FS: Forest Service	Rocky Mountain Research Station	Science & Space	The purpose of this tool is to produce samples that are well spread and balanced. This sample design will reduce the quantity of samples needed and further reduce costs associated with collecting field data.	The tool outputs data frames and geospatial-data-frames.	Stage 3 - Implementation (Use case is currently undergoing functionality and security testing)	Neither	5/1/2024	5/1/2024				No			Developed in-house	No agency-owned data used in this project.	None;
74	USDA-077: Regression, Classification, Clustering with Hilbert Curves	NRE: Natural Resources and Environment	FS: Forest Service	Rocky Mountain Research Station	Science & Space	The purpose of this tool is to perform better regression, classification, and clustering. This will create a new and better ways to produce various estimates, reducing cost and error.	The tools will output Data frame and Raster Surfaces/maps.	Stage 3 - Implementation (Use case is currently undergoing functionality and security testing)	Neither	6/1/2024	6/1/2024				No			Developed in-house	No agency-owned data used in this project.	None;
75	USDA-079: The Big Data, Mapping, and Analytics Platform (BIGMAP) Project	NRE: Natural Resources and Environment	FS: Forest Service	Research and Development	Science & Space	The purpose of this project is to use geospatial predictions from Forest Inventory and Analysis samples to make more accurate estimates of different forest characteristics. Greater precision in estimates leads to more informed decisions about the forest resources in the US.	The model outputs predictions in the form of raster maps.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	1/1/2019	1/7/2019	7/5/2021			No			Developed with both contracting and in-house resources	Data from the Forest Inventory and Analysis program and the Forest Inventory and Analysis Database were used to finetune and validate the models used. While the data are publicly available, the actual coordinates of the plot locations used are not, in order to protect the confidentiality of the land owner.	None;
76	USDA-080: BirdNET to detect bird vocalizations for research and species monitoring	NRE: Natural Resources and Environment	FS: Forest Service	Research and Development	Science & Space	BirdNET quickly scans thousands of hours of forest audio recordings to detect bird calls from species that are important for forest monitoring, like spotted owls, black-backed woodpeckers, and willow flycatchers. This decreases the time and cost associated with manually listening to recordings to identify bird calls.	The model outputs text files of bird calls, which include the bird species and time that the call was recorded.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	6/1/2021	6/1/2021	2/13/2023		https://www.sciencedirect.com/science/article/pii/S1574954121000273	No			Developed in-house	BirdNET was developed using data from the MacAulay Library of Natural Sounds at Cornell University. Forest Service did not provide any contract or data. BirdNET is publicly available.	None;
77	USDA-081: Hurricane impact descriptions	NRE: Natural Resources and Environment	FS: Forest Service	Forest Inventory and Analysis; Southern Research Station	Mission-Enabling (Internal Agency Support)	The purpose of the AI model is to convert a table of data about a tropical cyclone's path and estimated impact on forests into a clear and understandable story. This is part of a rapid assessment given to stakeholders after a cyclone hits, so it needs to be done fast. We are creating a tool to automate this process and the AI helps to make better quality reports.	The model outputs a few paragraphs of easy-to-read text that explains the effects of a cyclone.	Stage 2 - Development and Acquisition (AI use case is currently under development with the necessary IT tools and data infrastructure having been provisioned)	Neither	7/1/2024	7/1/2024				No			Developed in-house	No agency owned data is used to train, fine-tune, or evaluate the performance.	None;
78	USDA-082: Predictive flood modeling	NRE: Natural Resources and Environment	FS: Forest Service; USDOT: US Department of Transportation	Southern Research Station-4353	Transportation	The purpose of this tool is to predict water flow during floods and assesses the vulnerability of drains under roads. This will help U.S. Department of Transportation (USDOT) and USDA Forest Service to make informed decisions in drain restoration and protection against flooding.	The model outputs water flow predictions during flood events and the vulnerability level of drains under roads.	Stage 1 - Initiation (The use case's intended purpose and high-level requirements are documented)	Neither	10/1/2024									No agency-owned data used in this project.
79	USDA-083: FuelCast	NRE: Natural Resources and Environment	FS: Forest Service	Rocky Mountain Research Station	Emergency Management	This project predicts future fuel conditions and gives early warnings to help plan fuel management. The benefits include better preparation of the US firefighting teams for potential increases in large wildfires. This system also reduces the workload for fire behavior analysts because it provides fuel estimates, so they don't have to spend as much time figuring out fire behavior patterns through trial and error.	The model outputs predictions of the future quantity of wood and plants that could be present and contribute to wildfires.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	10/17/2019	10/17/2020	10/18/2020			No			Developed with both contracting and in-house resources	No agency-owned data used in this project.	None;
80	USDA-084: R1 Forest Vegetation Modeling	NRE: Natural Resources and Environment	FS: Forest Service	Northern Region (R1)	Energy & the Environment	The purpose of this tool is to use satellite images and methods such as LiDAR (light detection and ranging) with machine learning to model forest vegetation and make estimates. The use of machine learning improves models and estimates with decreased time and cost.	The model outputs predictions of forests and vegetation in the form of raster and vector geospatial maps.	Stage 1 - Initiation (The use case's intended purpose and high-level requirements are documented)	Neither	1/1/2024									No agency-owned data used in this project.
81	USDA-085: ESRI Support Chat Bot	NRE: Natural Resources and Environment	FS: Forest Service	Geospatial Office	Science & Space	The purpose of this chatbot is to help handle requests for support with geospatial software between our team and the software vendor. 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.	The chatbot outputs support ticket entries, code snippets for queries, and text and links for support ideas and answers.	Stage 1 - Initiation (The use case's intended purpose and high-level requirements are documented)	Neither	10/21/2024									No agency-owned data used in this project.
82	USDA-086: Wildlife deterrent system	NRE: Natural Resources and Environment	FS: Forest Service; Forest Service Research & Development	Southern Research Station	Science & Space	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. 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.	The model performs video object detection of a coyotes, and arms an electrical barrier to prevent passage of the coyote.	Stage 3 - Implementation (Use case is currently undergoing functionality and security testing)	Neither	8/26/2021	8/26/2021				No			Developed with contracting resources	The data is composed of a few thousand photographs of coyotes collected from trail cameras. Images were used by the contractor to train the system to detect coyotes.	None;
83	USDA-087: The Lost Meadows Model	NRE: Natural Resources and Environment	FS: Forest Service	Pacific Southwest Research Station	Energy & the Environment	The purpose of this model is to find out where meadows used to be and how often they appeared in order to understand their original state and their potential for restoration. The discovery of these areas increases the potential for meadow restoration, which can benefit biodiversity, wildfire management, carbon storage, and water storage.	The model outputs predictions of areas with meadow-like environmental conditions. The predicted areas include a mixture of existing but undocumented meadows, non-meadow lands that may have once been meadows, and meadow-like areas that may never have been a meadow.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	10/10/2022	10/10/2022	7/1/2023			No			Developed in-house	The data is a collection of 11,127 publicly available meadow polygons from the Sierra Nevada MultiSource Meadow Polygons Compilation, Version 2 (UC Davis and USDA Forest Service 2017).	None;
84	USDA-088: Markov random fields for mixed forests	NRE: Natural Resources and Environment	FS: Forest Service	Pacific Southwest Research Station	Science & Space	The purpose of this tool is to improve the accuracy of estimates in machine learning models. The benefits include helping stakeholders make more informed and effective decisions for managing mixed forests.	The model outputs predicted counts of tree species in a location, and the degree of competition between different tree species in the same location.	Stage 2 - Development and Acquisition (AI use case is currently under development with the necessary IT tools and data infrastructure having been provisioned)	Neither	10/1/2022	10/1/2022				No			Developed in-house	The data is comprised of ForestGEO tree count data from the Republic of Palau.	None;
85	USDA-089: AI for regional forest mapping and monitoring	NRE: Natural Resources and Environment	FS: Forest Service	Pacific Northwest Research Station	Energy & the Environment	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. This information will help land managers be more effective and efficient with their planning.	The model outputs GeoTiffs (raster maps of forest attributes, such as tree density and tree species data).	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	1/1/2000	1/1/2000	10/1/2002			No			Developed with contracting resources	Forest Inventory and Analysis data collected by the USDA Forest Service (Research & Development). It contains 200,000 individual plot measurements, and hundreds of Geospatial Information System (GIS) features (climate data, satellite images, topography, etc.) stored as 30-meter raster maps (GeoTiffs).	None;
86	USDA-090: IOL Focus Group and Survey Sensemaking	NRE: Natural Resources and Environment	FS: Forest Service	WO Research and Development	Education & Workforce	The purpose of this model is to efficiently process large quantities of focus group transcripts and survey results. Benefits include decreased labor hours manually processing transcripts and surveys.	The model outputs text summaries of focus group comments and surveys.	Stage 1 - Initiation (The use case's intended purpose and high-level requirements are documented)	Neither	6/11/2024									No agency-owned data used in this project.
87	USDA-091: Esri ArcGIS Pro Deep Learning Modules	NRE: Natural Resources and Environment	FS: Forest Service	Geographic Information System (GIS) Stakeholder Community - all deputy areas	Mission-Enabling (Internal Agency Support)	The purpose of this tool is to enhance scientific modeling and analysis, which will standardize Geographic Information System (GIS) workflows for modeling and analytics.	The tools will output image classifications.	Stage 4 - Operation and Maintenance (Use case is integrated into agency operations, and is being monitored for performance)	Neither	4/1/2024	4/1/2024	4/1/2024			No			Developed in-house	The data is comprised of Natural Resource image data.	None;
88	USDA-092: EMC Comment Parsing and Analysis	NRE: Natural Resources and Environment	FS: Forest Service	National Forest System; Ecosystem Management and Coordination	Government Services (includes Benefits and Service Delivery)	This project aims to extract, categorize, and respond to public comments based on past responses. The benefits include creating a standardized process for handling comments, making public comment data more accessible and ready for AI use, reducing the time and cost of processing comments, minimizing human errors due to high workloads and tight deadlines, improving responsiveness to public concerns, increasing public trust, enhancing accountability through clear reporting, and supporting team training by building a database of common themes and response strategies.	The model will output text analyses of categories pulled from public comments and recommend responses based on historic responses.	Stage 1 - Initiation (The use case's intended purpose and high-level requirements are documented)	Neither	3/1/2024									No agency-owned data used in this project.
89	USDA-093: QUIC-Fire processing and analysis	NRE: Natural Resources and Environment	FS: Forest Service	Northern Research Station	Science & Space	AI is being used to analyze data from fire-atmosphere models to understand fire behavior and effects. The goal is to create tools that will help fire and smoke managers use the QUIC-Fire (Quick Urban & Industrial Complex-Fire) model for planning controlled burns and assessing wildfire behavior.	The AI output will be a collection of metrics that provide building blocks for a tool that fire and smoke managers will use to implement QUIC-Fire (Quick Urban & Industrial Complex - Fire) into their decision-making.	Stage 1 - Initiation (The use case's intended purpose and high-level requirements are documented)	Neither	9/25/2024									No agency-owned data currently used in this project.
90	USDA-094: Analysis of prescribed fire turbulence data	NRE: Natural Resources and Environment	FS: Forest Service	Northern Research Station	Science & Space	The purpose of this project is to find connections between the heat from a wildfire and the turbulence it creates in the air. Current tools are not very accurate and can make mistakes. This AI effort helps create better tools that can assist fire and smoke managers in making decisions about smoke management.	The model outputs correlation analysis of how temperature change is associated with air turbulence measurements above a prescribed fire.	Stage 2 - Development and Acquisition (AI use case is currently under development with the necessary IT tools and data infrastructure having been provisioned)	Neither	6/12/2023	7/10/2023				No			Developed in-house	The data is comprised of temperature and turbulence kinetic energy readings collected over time from 16 sonic anemometers (measure wind speed and direction) and 112 thermocouples (measures temperature). The datasets are published in the US Forest Service data archive.	None;