AI use cases in agriculture
Nine things farmers and agricultural extension workers actually use YieldAI Global for. Each one names the modules behind it, and every one of those modules is a screen that exists in the product today.
You will not find an outcome statistic anywhere on this page. We have not run the studies that would support one, and a number we cannot source is worth less than the sentence it sits in.
Who is this for?
A smallholder household
Crop advice in the language the household actually speaks, and the government mandi price checked against the Minimum Support Price before agreeing a sale.
An extension worker
Farmer records, field visits and follow-ups in one dashboard instead of scattered across notebooks and spreadsheets, with USDA market information alongside.
A grain farmer
The weather and irrigation view for planning field work, and Canadian market prices in the same place, without switching between several sites.
What can it do today?
Identify what is wrong with a plant from a photograph
Photograph an affected leaf and get the likely disease, the visible symptoms behind that read, and a confidence level stated honestly rather than rounded up to certainty. Guidance comes back in the farmer’s own language.
Decide what to plant on a given field
Soil figures and local conditions feed a crop recommendation, so the choice is grounded in what the ground will actually carry rather than in what did well for a neighbour.
Check what a crop is worth before agreeing a sale
Government market prices for the relevant country, each carrying the date it was published. In India the figure is benchmarked against the Minimum Support Price, so a farmer can see whether an offer sits above or below the state floor.
Find a government scheme and work out whether you qualify
Schemes across India, the USA and Canada with the benefit, the eligibility rules and the application route, each checked by hand against the official source. There is no API for this data anywhere, so it is curated deliberately and kept current.
Plan irrigation around the weather that is actually coming
Forecast and field conditions together, so watering decisions follow the week ahead instead of a fixed schedule set at the start of the season.
Keep farmer records straight across many holdings
Built for the extension worker rather than only the individual grower: farmer records, field visits and follow-ups in one place. One advisor often carries around 1,200 farming households, which is the constraint the whole product is shaped around.
Ask a question out loud, in your own language
Fourteen languages across the whole interface, not only the chatbot, with voice input and voice output. Someone can speak a question and hear the answer, which matters when reading is the harder half.
See pest pressure before it arrives
Pest prediction against local conditions, so the warning lands while there is still a decision to make rather than after the damage is visible.
Estimate yield and track a season
Yield prediction alongside the season’s own records, so the estimate can be read against what the field has actually done rather than in isolation.
What it will refuse to answer
Asked for a chemical dose or a spray rate, YieldAI Global declines and routes the farmer to a local agriculture extension officer or Krishi Vigyan Kendra. It does not estimate, hedge or give a range.
Most AI products are built to always answer. This one is built to recognise the questions where a confident wrong number costs a household a season, and to hand those to a person who is accountable for the answer. It is the most distinctive thing about the product and the thing we are least willing to trade away.
Where do the market prices come from?
Named government sources, not the model. Cadence differs by country and the product shows the date each figure was published, because a stale price without a date is a trap and a stale price with one is still useful.
| Country | Source | Update cadence |
|---|---|---|
| India | Agmarknet, via data.gov.in | Daily |
| USA | USDA NASS Quick Stats | Monthly |
| Canada | Statistics Canada table 32-10-0077 | Monthly |
What this page does not claim
- No customer count, user count, revenue, funding or partnership. None have been established, and AGRIVISION AI is early-stage, founder-led and pre-revenue.
- No accuracy percentage for disease detection, and no yield-improvement figure. We intend to publish field-validated numbers when we have results worth standing behind.
- No crop count. A figure of 54 circulated previously and nothing in the codebase supports it.
- No claim to be the first or only AI agriculture platform. Cropin, Farmonaut and others were serving this sector earlier.
- FieldSense and FieldOps appear nowhere above, because neither is built. They are roadmap, and a use-case page is exactly where roadmap quietly becomes a promise.
Try it, or tell us where it falls short
YieldAI Global is live as a web app in India, the USA and Canada, with a 30-day free trial. The mobile app has not launched. If you work in agriculture, extension or agronomy and it fails you somewhere, that is the message we most want to receive.
hello@agrivisionai.orgPhotography licensed under the Pexels licence and self-hosted, via Pexels contributors: the rice transplanting and the rice harvest in Patna, the mango orchard, the canola field in Saskatchewan, and the plant and smartphone by Mark Stebnicki. A location is named only where Pexels documents one. None of these photographs shows the product.