Automation index
How much of a Pesticide applicator’s job can AI already do?
A farm log comes earlier. Treating without taking the neighbour or the river do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Pesticide applicator
22%
AI can already do 22% of this job.
#319 / 633
View rankingPesticide applicator
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Log, product and the dose.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Apply, drift and the wind that gets up.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 12% | +2.2 |
The withholding period and the one who wants in now.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
PPE, the container and the collection point.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The neighbour, the bees and stopping in time.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Not taking the river, and signing the treatment.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- farm log
- pesticide dose
- drift
- withholding period
- PPE
- empty containers
- bees
- field wind
Sources for this job· O*NET 37-3012.00· ESCO/ISCO 6113
IS WORK OVER?
ML engineer
29%
#64 / 633
View rankingML engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511