USDA · USDA-071 · record R1372
Forest disease detection and screening
Department of Agriculture · Natural Resources and Environment
Overview
- Development stage
- In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
- High-impact designation
- Not high-impactSource: c) Not high-impact
- Impact justification
- Not reported
- Start date
- 2020-08-03 (day precision)Source: 8/3/2020
- Withheld from public reporting
- Not reported
Mission
- Topic area
- Science
- Operational functions
- Clinical and medical Derived by keyword rules; see methodology
Problem the AI is intended to solve
To improve tree disease diagnosis and screening.
Expected 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.
System outputs
Prediction (e.g., tree is diseased or not diseased, tree is resistant or susceptible).
Technology
- AI classification
- Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
- System name(s)
- Not reported
- Custom-developed code
- No
- Public source code
- Not reported
Sourcing
- How it was built
- Developed in-houseSource: b) Developed in-house
- Vendor (as reported)
- Not reported
- Vendors (standardized)
- None on the standard list
A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.
Data and privacy
- Involves PII
- No
- Privacy Impact Assessment
- Not reported
- Authorization to Operate
- No
- Demographic features
- k) None of the above
- Training and evaluation data
- Research datasets containing spectral and image data and data on tree disease status, tree size, tree genotype, tree location. Datasets are generated through research activities conducted by and in collaboration with Forest Service and university personnel.
- Federal Data Catalog
- Not reported
Governance
0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.
Pre-deployment testingNo answer reported
AI impact assessmentNo answer reported
Independent reviewNo answer reported
Ongoing monitoringNo answer reported
Operator trainingNo answer reported
Fail-safeNo answer reported
Appeal processNo answer reported
User and public consultationNo answer reported
Potential impacts and how they were identified
Not reported