Target
Define weeds, disease, nutrient state, crop stage and threshold.
Real-work map · 10
Precision treatment
Evaluate targeted weeding, spraying or input placement through detection, decision, delivery, drift, efficacy and crop response.
Evidence statusField evidence framework · crop, season, site and operating-envelope validation requiredLast reviewed: 16 August 2026
10
FUURAA thesis
Using less input is beneficial only when control efficacy, crop safety and environmental transfer remain acceptable.
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.
Delivery
Verify placement, rate, latency, drift and shutoff.
Outcome
Measure efficacy, retreatment, crop injury, residues and yield.
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
What baseline application is being reduced?
- 02
Are misses and retreatments included?
- 03
How are wind, speed and nozzle state controlled?
- 04
What later observation confirms efficacy?
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
Target and treatment ground truth
- 02
Evidence package 2
Material-use, drift and shutoff record
- 03
Evidence package 3
Efficacy, crop-injury and yield follow-up
Scope boundary
State what this evidence still cannot be generalised to.
- 01
Input savings without efficacy, crop outcome and a declared baseline can hide under-treatment or shifted costs.
- 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 · Published 2026-04-20
↗Call for representative AI + Agriculture application scenarios
Prioritises narrow, deeply applied, replicable scenarios across agricultural research, production, operations and public services.