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Farmer — tasks, one by one
The unit of analysis is the task, not the job title. Each one below carries its direction, whether the judgement rests on evidence or on platform inference, the reasoning, and what it does not establish.
Every task on this page#
Spraying crops
Automating✓ Evidence-backedApplying pesticide, fungicide and fertiliser across the fields at the right time, traditionally with a backpack or tractor-mounted sprayer.
This is the field task that machines have most visibly taken over. Japan's agriculture ministry estimates that drones sprayed pesticide and fertiliser over more than a million hectares of cumulative area in fiscal 2023 and about 1.2 million in fiscal 2024, with some 3,000 to 4,000 spraying drones sold each year and over 1,400 pesticides now registered for drone application. The walking and carrying moves to a machine that flies a set route; a person still plans it and operates it.
The areas are the ministry's own estimates, made by methods that changed between years (and not counted at all for one year), so they show scale, not a reliable growth rate. Cumulative sprayed area counts the same field more than once, and nothing here says how many farmers use drones or hire a spraying service instead.
Ploughing, planting and harvesting with machines
Being augmented≈ Platform inferenceDriving tractors, planters and combines through the season's field operations.
Auto-steering and robot tractors are the next step after mechanisation, and in Japan a 2024 law now funds farmers who adopt such technology together with new ways of producing, citing the fall in the number of farmers as its reason. Where the machine steers itself, one person can supervise more of the field work.
A law that funds adoption does not show adoption happened; how many farmers have been certified under it, and what they bought, is not established here.
Deciding what to plant, when and how
Still human-led≈ Platform inferenceChoosing crops and varieties, timing planting, spraying and harvest against weather and prices, and judging the state of the crop in the field.
Sensors, satellite images and forecasts give a farmer more to decide with, but the decision carries the farmer's own money and a season that cannot be repeated, and it rests on local knowledge of a particular field. Tools inform it; the farmer makes it and bears the result.
This rests on the nature of the work rather than on a record, and decision-support services are spreading, particularly through the service providers the same ministry promotes.
Picking and handling fruit and vegetables by hand
Still human-led≈ Platform inferencePicking, cutting, sorting and packing crops that bruise easily or ripen unevenly.
Delicate crops are the hardest thing to mechanise: a robot has to find each fruit, judge whether it is ripe and pick it without damage. Picking robots exist for some crops, but most fruit and vegetable harvesting is still done by hand, and at harvest it needs many hands at once.
This rests on the state of the technology rather than on a record, and the same law also certifies and supports companies that develop and supply smart-farming technology, so it may change for specific crops.
Caring for livestock
Being augmented≈ Platform inferenceFeeding, milking and checking the health of animals every day.
Milking robots and automatic feeders already do the most repetitive parts of dairy work where they are installed, and sensors flag sick animals earlier. The daily judgement about an animal's health, and the work when something goes wrong, stays with the farmer.
This page's sources are about crops, not livestock; this judgement rests on the equipment in use rather than on a record.
Running the farm's paperwork and sales
Being augmented≈ Platform inferenceKeeping records, applying for subsidies and certifications, and selling the harvest.
Farm-management software and online sales channels take over record-keeping and reach buyers directly. Applying for support is itself becoming a task: in Japan, getting the financial support the smart-farming law offers means submitting a plan for certification.
This rests on the tools available rather than on a record of how widely they are used.