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Food

Identify fruits, vegetables, dishes, and ingredients from photos.

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What is in this section

How to use the food section

There are 11 articles here , published between January 2026 and March 2026, and they answer questions rather than catalogue species: how to tell two similar things apart, what a particular mark or shape means, what to do once you know. They are the long form.

The faster route, when you have the thing in front of you, is one of the 2 free keys for this section — 58 entries between them. A key does not ask you to describe what you are looking at in words; it asks a series of questions you can answer by looking, and the list shrinks as you go. Nothing is hidden behind the filter: every entry is written into the page, so the whole catalogue is readable with scripting switched off, and each one carries the single sentence that separates it from its nearest look-alike.

There is a third thing on the site worth knowing about from here, because it answers a question this section cannot. People arriving at an identification problem usually end up comparing apps, and the honest answer to "which one" depends on facts that change — what each costs this month, what it locks behind a subscription, who publishes it. Those pages exist under compare and alternatives, and not one figure on them is typed by hand: every price and every in-app purchase list is read from a dated App Store snapshot, and the build fails if a currency amount ever appears in the editorial text. It is the same discipline as the numbers below, applied to a subject that goes stale faster.

Reading a shortlist without being fooled by it

Whatever brings you here, the failure mode is the same and it is worth naming. A key, an app and a search all hand you a shortlist, and a shortlist feels like an answer. It is not: it is a set of candidates that survived the characters you happened to check, and the one at the top is usually the one that is common rather than the one that is right. That is often the same thing, which is exactly why it is dangerous when it is not.

Three habits fix most of it. Check a character you have not used yet — if the candidate survives a test drawn from a different part of the organism, the identification is much stronger than one built on colour alone. Check where you are: an enormous share of confident misidentifications are species that do not occur within a thousand miles of the observer, and geography alone settles them. And notice when the answer does not matter — a great many identifications are curiosity, where being wrong costs nothing, and the small number where it does cost something are worth treating completely differently.

Where the consequence of an error is medical — anything eaten, anything that bit somebody, anything a child had in their hands — no page here and no app is the right tool. Describe what you have to somebody qualified, and keep the specimen or a photograph. The keys are built to make that description accurate, which is a genuinely useful thing to be, and it is a different job from telling you it is safe.

Open data

What the observation record actually says about food

Orvik's free keys for this section cover 43 named species, and each of them is matched against GBIF, the international register of biodiversity records. Between them they stand on 6,801,354 georeferenced records spanning 31 families and 17 orders, the oldest specimen collected in 1945. None of these figures is typed into the page: they are read from a dated snapshot, 2026-09-04, and every one is checkable against the source. A further 11 entries in these keys name a genus or a family rather than a single species; their counts already contain everything beneath them, so they are left out of this total rather than added to it.

Most of that record comes from France, the United Kingdom and the United States — which is a fact about who uploads observations, not about where these food live, and it is worth holding on to when a key looks confident about a species' range. Half of every record for this group falls in May–August. The figure below separates the two effects: the bars are the raw monthly share, which largely tracks when people go outdoors, and the line divides each month by this corpus baseline — every species in every Orvik key — so what remains is what is particular to this section rather than to the seasons of nature-watching itself.

Share of 5,738,547 GBIF records of 43 food by month of the year, against the seasonal average for all Orvik records 0%4%8%11%15%JFMAMJJASOND share of records vs. all Orvik records 1.0 = the seasonal average
Half of the 5,738,547 georeferenced records for these 43 food fall in May–August alone. The bars are the raw share by month, which mostly measures when people go looking; the line divides each month by the baseline for every species in every Orvik key, so a value above 1.0 means these food are reported more than the season alone explains, and it peaks in August. Source: GBIF, snapshot 2026-09-04.

Best-evidenced food in our keys

Ranked by how much evidence exists rather than by how likely you are to meet one, because those are different questions and only the first can be measured. A high count is a good prior when a key narrows to two candidates and you have to pick.

The twelve best-recorded species across Orvik's food keys, snapshot 2026-09-04. Source: GBIF and iNaturalist. Free to reuse with a link.
Species GBIF records Half fall in In the key
Raspberry and black raspberry 799,549 May–Jul wild berry identifier
Wild strawberry 798,077 May–Jul wild berry identifier
Juniper 797,120 Jun–Aug wild berry identifier
Bittersweet nightshade 746,681 May–Jul wild berry identifier
Lily of the valley 476,918 Apr–Jun wild berry identifier
Black nightshade 363,094 Jul–Sep wild berry identifier
Fly agaric 316,522 Oct–Oct mushroom identifier
Blackberry 255,561 Mar–Jul wild berry identifier
Snowberry 209,919 May–Aug wild berry identifier
Turkey tail 197,063 Aug–Dec mushroom identifier
Pokeweed 195,553 Jul–Sep wild berry identifier
Shaggy ink cap 148,708 Oct–Oct mushroom identifier

Every figure on this page comes from the same snapshot, and the whole thing is published as one downloadable file: open field data, CC BY 4.0, with each source's own licence listed. If a number here disagrees with the source database today, the source is right and this snapshot is old.

Free keys in this section: What berry is this, and can you eat it? · What mushroom is this?

How do I identify food? Start by observing shape, color, texture, smell, and growth context—whether it’s on a tree, vine, shrub or in a garden bed—and compare those features to reliable references. Photographs should show multiple angles, close-ups of leaves or stems, and any distinctive marks like seeds, pits or surface glands. Note time of year and location, and if you plan to eat it, confirm edibility with species-level identification and cross-check toxin warnings. Combining careful field notes with high-quality images is the most reliable approach to food identification.

How Orvik helps identify foods

Orvik uses a visual AI trained on labeled photos and species data to match your pictures to likely food candidates, then ranks results by confidence and supporting traits. The app highlights key diagnostic features from your photo—leaf shape, berry cluster pattern, seed or pit presence—and cites similar verified records so you can compare. For uncertain matches, Orvik offers clarifying prompts to guide additional photos that improve accuracy. As a food identifier app, Orvik also stores your verified observations for later review and export.

What you can learn here

On this Food hub you’ll find species-level ID, common look-alikes, usage notes, and clear safety guidance including toxicity flags and parts to avoid. Orvik provides identification, calorie estimates when available, and links to handling or preparation best practices; it does not replace professional toxicology advice, so when risk is present cross-check with local experts before consuming. The hub covers wild berries, garden vegetables, fermented products and packaged items so you can verify edibility and provenance.

Frequently Asked Questions

How accurate is Orvik at identifying food from photos?
Orvik provides confidence scores and ranked matches, but accuracy depends on photo quality and visible diagnostic features. Well-lit, close-up photos of fruit, leaves and stems plus location and season often give reliable genus- or species-level IDs; for high-risk or unfamiliar items always confirm with a local expert.
Is Orvik free to use?
Basic identification features are free to use, including single-photo lookups and confidence feedback. A subscription unlocks advanced features such as batch processing, export tools, offline packs and priority support.
Can Orvik tell if a wild berry is poisonous?
Orvik flags species that are known to be toxic and highlights parts to avoid when that information is available. These flags are reference guidance only—if there is any doubt about safety, consult local poison control or a trained botanist before consuming.
What can I do to improve identification accuracy?
Take multiple clear photos from different angles: whole plant, leaf surfaces, fruit close-ups, stem and surrounding habitat, and include scale where possible. Add date and location, note smell or texture, and follow Orvik’s prompts for missing diagnostic shots to raise confidence levels.
Does Orvik provide calorie or nutritional estimates?
Orvik can supply approximate calorie estimates or link to nutritional profiles for common foods when those data exist in its reference sets. These are intended as general guidance and may not reflect unique cultivars, preparation methods, or exact portion sizes.
Can I use Orvik offline and share my identifications?
Orvik offers downloadable offline packs for certain regions and species with a subscription to support fieldwork without cell service. You can also export or share verified observations and images as reports or CSV files for research, records, or local verification.

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