A farmer in Estonia says AI has gone from failing to compile her Excel data in spring to creating her spreadsheets by harvest. The same report describes AI being tested for food-quality checks and autonomous machinery still in development, showing both where agricultural AI is useful now and where it has yet to leave the test field.
Watch Desk analysis
What happened
Airika Verrev, a crop and livestock production manager and consultant at Pajo Ranch, told ERR News she now asks AI to create spreadsheets and uses it to analyse data and find relevant legislation. She says the information still needs constant checking.
The report also describes an AIRE demonstration project testing computer vision to spot packaging problems on a meat-production conveyor, including incorrect dates, poor labels and visible defects. Separately, a field robot at the Estonian University of Life Sciences remains in testing. Its developers still need to teach it field boundaries and where it can operate. The report says legal restrictions and safety needs also limit the use of agricultural drones.
Why it matters
These examples put some substance behind the phrase “AI in agriculture”. For one farm, the immediate use is office work and data analysis; in a factory, it is checking products on a conveyor. The more ambitious prospect is combining years of farm records with field data to help make decisions such as how much fertiliser or crop-protection product to order.
That last step remains a possibility, not a demonstrated saving. The report says farmers need to assess whether the benefits justify the investment, and the relevant agricultural expertise still matters when deciding what data to use and whether an answer makes sense.
Our read
The promising story here is not that software can replace a farmer. It is that some repetitive work may become easier, while people with knowledge of the fields remain responsible for defining the problem and checking the result. Even the tractor is still learning where it is allowed to go. A sensible start, the researchers suggest, is a chatbot and a specific question, followed by small-scale tests of recommendations against real farm data.
What to watch
- Whether farmers can show that AI-assisted analysis saves money or improves decisions in practice.
- Whether the field robot moves beyond testing, and how its operating limits are established.
- Whether drone rules change and allow more agricultural uses under stated safety conditions.
- Whether the conveyor-belt inspection system is adopted beyond its demonstration project.
Discussion spark: Should farmers start with low-stakes office tasks such as spreadsheets, or prioritise testing AI on field decisions where the potential savings may be larger?
Sources and evidence
- Source update (18 October 2026, 05:43 UTC)
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