Sample
Define plots, timing, route, missingness and representativeness.
Real-work map · 12
Phenotyping and surveillance
Turn repeated ground or aerial observations into calibrated, traceable and decision-relevant crop measurements.
Evidence statusField evidence framework · crop, season, site and operating-envelope validation requiredLast reviewed: 16 August 2026
12
FUURAA thesis
More images do not create evidence unless sampling, metadata, calibration and ground truth remain comparable.
Engineering map
Turn the title into engineering objects that can be observed, measured and reviewed.
Each layer states the boundary to establish and the evidence needed for the next decision.
Measure
Calibrate sensors and link traits to reference methods.
Use
State the breeding, scouting or management decision supported.
Verification questions
Write the questions first, then decide whether a demo, test, pilot or operating record can answer them.
Each question needs an object, conditions, denominator, threshold and accountable decision owner.
- 01
Is the route representative or convenience-based?
- 02
How are changing light and plant size normalised?
- 03
Which reference measurement validates each trait?
- 04
Can another system reproduce the dataset?
Evidence to preserve
Enable the next reader to reconstruct conditions, results, failures and the decision.
A conclusion alone loses reviewability; raw records, configuration and exclusions matter too.
- 01
Evidence package 1
Sampling plan and missing-data log
- 02
Evidence package 2
Calibration and reference-method record
- 03
Evidence package 3
Versioned dataset with decision linkage
Scope boundary
State what this evidence still cannot be generalised to.
- 01
A research platform producing useful images is not automatically a validated diagnostic or commercial agronomy product.
- 02
This is an engineering reading and decision framework; it does not replace application-specific risk assessment, conformity work, procurement acceptance or professional advice.
Sources and evidence status
Read standards scope, measurement evidence and application conclusions separately.
Source dates and review status remain visible; external sources open in a new tab.
01
USDA Agricultural Research Service · Programme page verified 2026-08-16
↗02Digital Agriculture Systems Hub (DASH)
Connects standardised metadata, automated deployment, stakeholder design and field research tools such as autonomous crop-imaging platforms.
Food and Agriculture Organization of the United Nations · Published 2022-11-02
↗03The State of Food and Agriculture 2022 — Leveraging automation
Assesses productivity, resilience, sustainability, labour, access and inequality across automation technologies and farming contexts.
China Ministry of Agriculture and Rural Affairs · Policy interpretation published 2024-10-28
↗National Smart Agriculture Action Plan (2024–2028)
Sets public-service, application-expansion and demonstration actions while emphasising standards, data, services and practical adoption.